Evaluating the performance of binary predictions

(Updated 2015 06 06)

Background: This blog posting has its origins in a recent review of a QCA oriented evaluation, in which a number of hypotheses were proposed and then tested using a QCA type data set. In these data set cases (projects) are listed row by row and the attributes of these projects are listed in columns. Additional columns to the right describe associated outcomes of interest. The attributes of the projects may include features of the context as well as the interventions involved. The cell values in the data sets were binary (1=attribute present, 0= not present), though there are other options.

When such a data set is available a search can be made for configurations of conditions that are strongly associated with an outcome of interest. This can be done inductively or deductively. Inductive searches involve the uses of systematic search processes (aka algorithms), of which there are a number available. QCA uses the Quine–McCluskey algorithm. Deductive searches involve the development of specific hypotheses from a body of theory, for example about the relationship between the context, intervention and outcome.

Regardless of which approach is used, the resulting claims of association need evaluation. There are a number of different approaches to doing this that I know of, and probably more. All involve, in the simplest form, the analysis of a truth table in this form:

In this truth table the cell values refer to the number of cases that have each combination of configuration and outcome. For further reference below I will label each cell as A and B (top row) and C and D (bottom row)

The first approach to testing is a statistical approach. I am sure that there are a number of ways of doing this, but the one I am most familiar with is the widely used Chi-Square test. Results will be seen as most statistically significant when all cases  are in the A and D cells. They will be least significant when they are equally distributed across all four cells.

The second approach to testing is the one used by QCA. There are two performance measures. One is Consistency, which is the proportion of all cases where the configuration is present and the outcome is also present (=A/(A+B)). The other is Coverage, which is the proportion of all outcomes that are associated with the configuration (=(A/(A+C)).

When some of the cells have 0 values three categorical judgements can also be made. If only cell B is empty then it can be said that the configuration is Sufficient but not Necessary. Because there are still values in cell C this means there are other ways of achieving the outcome in addition to this configuration.

If only cell C is empty then it can be said that the configuration is Necessary but not Sufficient. Because there are still values in cell B this means there are other additional conditions that are needed to ensure the outcome.

If cells B and C are empty then it can be said that the configuration is both Necessary and Sufficient

In all three situations there only needs to be one case to be found in a previously empty cell(s) to disprove the standing proposition. This is a logical test, not a statistical test.

The third approach is one used in the field of machine learning, where the above matrix is known as a Confusion Matrix. Here there is a profusion of performance measures available (at least 13). Some of the more immediately useful measures are:

• Accuracy: (A+D)/(A+B+C+D), which is similar to but different from the Chi Square measure above
• True Positives: A/(A+B), also called Precision, which corresponds to QCA consistency
• True Negatives: D/(B+D)
• False Positives: C/(A+C)
• False Negatives: B/(B+D)
• Positive predictive value: A/(A+C), also called Recall, which corresponds to QCA coverage
• Negative predictive value: D/(C+D)

In addition to these three types of tests there are three other criteria that are worth taking into account as well: simplicity, diversity and similarity

Simplicity: Essentially the same idea as that captured in Occam's Razor. This is that simpler configurations are preferable, all other things being equal. For example: A+F+J leads to D is a simpler hypothesis than A+X+Y+W+F leads to D. Complex configurations can have a better fit with the data, but at the cost of being poor at generalising to other contexts. In Decision Tree modelling this is called "over-fitting" and solution is "pruning", i.e. cutting back on the complexity of the configuration.  Simplicity has practical value, when it comes to applying tested hypotheses in real life programmes. They are easier to communicate and to implement. Simplicity can be measured at two levels: (a) the number of attributes in a configuration that is associated with an outcome, (b) and the number of configurations needed to account for an outcome.

Diversity: The diversity of configurations is simply the number of different specific configurations in a data set. It can be made into a comparable measure by calculating it as a percentage of the total number possible. The total number possible is 2 to the power of A where A = number of kinds of attributes in the data set. A bigger percentage = more diversity.

If you want to find how "robust" a hypothesis is, you could calculate the diversity present in the configurations of all the cases covered by the hypothesis (i.e. not just the attributes specified by the hypotheses, which will be all the same). If that percentage is large this suggests the hypothesis works in a greater diversity of circumstances, a feature that could be of real practical value.

This notion of diversity is to some extent implicit in the Coverage measure. More coverage implies more diversity of circumstances. But two hypotheses with the same coverage (i.e. proportion of cases they apply to) could be working in circumstances with quite different degrees of diversity (i.e. the cases covered were much more diverse in their overall configurations).

Similarity: Each row in a QCA like data set is a string of binary values. The similarity of these configurations of attributes can be measured in a number of ways:

• Jaccard index, the proportion of all instances in two configurations where the binary value 1 is present in the same position i.e. the same attribute is present.
• Hamming distance, the number of positions at which the corresponding values in two configurations are different. This includes the values 0 and 1, whereas Jaccard only looks at 1 values

These measures are relevant in two ways, which are discussed in more detail further down this post:

• If you want to find a "representative" case in a  data set, you would look for the case with the lowest average Hamming distance in the whole data set
• If you wanted to compare the two most similar cases, you would look for the pair of cases with the lowest Hamming distance.

Similarity can be seen as a third facet of diversity, a measure of the distance between any two types of cases. Stirling (2007) used the term disparity to describe the same thing.

Choosing relevant criteria: It is important to note that the relevance of these different association tests and criteria will depend on the context. A surgeon would want a very high level of consistency, even if it was at the cost of low coverage (i.e. applicable only in a limited range of situations). However, a stock market investor would be happy with a consistency of 0.55 (i.e 55%), especially if it had wide coverage. Even more so if that wide coverage contained a high level of diversity. Returning to the medical example, a false positive might have different consequences to false negatives e.g. unnecessary surgery versus unnecessary deaths. In other non-medical circumstances, false positives may be more expensive mistakes than false negatives.

Applying the criteria: My immediate interest is in the use of these kinds of tests for two evaluation purposes. The first is selective screening of hypotheses about causal configurations that are worth more time intensive investigations, an issue raised in a recent blog.

• Configurations that are Sufficient and not Necessary or Necessary but not Sufficient. 
• Among these, configurations which were Sufficient but not Necessary, and with high coverage should be selected, 
• And configurations which were Necessary but not Sufficient, and with high consistency, should also be selected. 
• Plus all configurations that were Sufficient and Necessary (which are likely to be less common)

The second purpose is to identify implications for more time consuming within-case investigations. These are essential, in order to identify casual mechanism at work that connect the conditions that are associated in a given configuration. As I have argued elsewhere, associations are a necessary but insufficient basis for a strong claim of causation. Evidence of mechanisms is like muscles on the bones of a body, enabling it to move.

Having done the filtering suggested above, the following kinds of within-case investigations would seem useful:

• Are there any common casual mechanisms underlying all the cases  found to be Necessary and Sufficient, i.e those within cell A? 
• A good starting point would be a case within this set of cases that had the lowest average Hamming distance, i.e. one with the highest level of similarity with all the other cases. 
• Once one or more plausible mechanism were discovered in that case a check could be made to see if they are present in other cases in that set, this could be done in two ways: (a) incrementally, by examining adjacent cases, i.e cases with the lowest Hamming distance from the representative case, (b) by partitioning the rest of the cases, and examining a case with a median level Hamming distance, i.e. half way between being the most similar and most different cases.
• Where the configuration is Necessary but not Sufficient, how do the cases in cell B differ from those in cell A, in ways that might shed more light on how the same configuration leads to different outcomes? This is what has been called a MostSimilarDifferentOutcome (MSDO) comparison,
• If there are many cases this could be quite a challenge, because the cases could differ on many dimensions (i.e. on many attributes). But using the Hamming distance measure we could make this problem more manageable by selecting a case from cell A and B that had the lowest possible Hamming distance. Then a within-case investigation could find additional undocumented differences that account for some or all of the difference in outcomes. 
• That difference could then be incorporated into the current hypothesis (and data set) enabling more cases from cell B to now be found in cell A i..e Consistency would be improved
• Where the configuration is Sufficient but not Necessary,in what ways are the cases in cell C the same as those in cell A, in ways that might shed more light on how the same outcome is achieved by different configurations? This is what has been called a MostDifferentSimilarOutcome (MDSO) comparison,
• As above, if there are many cases this could be quite a challenge. Here I am less clear, but de Meur et al (page 72) say the correct approach is "...one has to look for similarities in the characteristics of initiatives that differ the most from each other; firstly the identification of the most differing pair of cases and secondly the identification of similarities between those two cases" The within-case investigation should look for undocumented similarities that account for some of the similar outcomes. 
• That difference could then be incorporated into the current hypothesis (and data set) enabling more cases from cell C to now be found in cell A i..e Coverage would be improved

How to select which hypotheses to test?

I have been reviewing an evaluation that has made use of QCA (Qualitative Comparative Analysis). An important part of the report is the section on findings, which lists a number of hypotheses that have been tested and the results of those tests. All of these are fairly complex, involving a configuration of different contexts and interventions, as you might expect in a QCA oriented evaluation.  There were three main hypotheses, which in the results section were dis-aggregated into six more specific hypotheses. The question for me, which has  much wider relevance, is how do you select hypotheses for testing, given limited time and resources available in any evaluation?

The evaluation team have developed three different data sets, each will 11 cases, and with 6, 6 and 9 attributes of these cases (shown in columns), known as "conditions" in QCA jargon. This means there are 2⁶ + 2⁶  + 2⁹ = 640  possible combinations of these conditions that could be associated with and cause the outcome of interest. Each of the hypotheses being explored by the evaluation team represents one of these configurations. In this type of situation, the task of choosing an appropriate hypotheses seems a little like looking for a needle in a haystack

It seems there are at least three options, which could be combined. The first is to review the literature and find what claims (supported by evidence) are made there about "what works" and select from these those that are worth testing e.g. one that seems to have wide practical use, and/or one that could have different and significant program design implications if it is right or wrong. This seems to be the approach that the evaluation team has taken, though I am not so sure to what extent they have used the programming implications as an associated filter.

The second approach is to look for constituencies of interest among the staff of the client who has contracted the evaluation.There have been consultations, but it is not clear what sort of constituencies each of the tested hypotheses have. There were some early intimations that some of the hypotheses that were selected are not very understandable. That is clearly an important issue, potentially limiting the usage of the evaluation findings.

The third approach is an inductive search, using QCA or other software, for configurations of conditions associated with an outcome that have both high level of consistency (i.e. they are always associated with the presence (or the absence ) of an outcome) and  coverage (i.e. they apply to a large proportion of the outcomes of interest). In their barest form these configurations can be be considered as hypotheses. I was surprised to find that this approach had not been used, or at least reported on, in the evaluation report I read. If it had been used but no potentially useful configurations found then this should have been reported (as a fact, not a fault).

Somewhat incidentally, I have been playing around with the design of an Excel worksheet and managed to build in a set of formula for automatically testing how well different configurations of conditions of particular interest (aka hypotheses) account for a set of outcomes of interest, for a given data set. The tests involve measures taken from QCA (consistency and coverage, as above) and from machine learning practice (known as a Confusion Matrix). This set-up provides an opportunity to do some quick filtering of a larger number of hypotheses than an evaluation team might initially be willing to consider (i.e. the 6 above). It would not be as efficient a search as the QCA algorithm, but it would however be a search that could be directed according to specific interest. Ideally this directed search process would identify configurations that are both necessary and sufficient (for more than a small minority of outcomes). A second best result would be those that are necessary but insufficient, or vice versa. (I will elaborate on these possibilities and their measurement in another blog posting)

The wider point to make here is that with the availability of a quick screening capacity the evaluation team, in its consultations with the client, should then be able to broaden the focus of useful discussions away from what are currently quite specific hypotheses,  and towards the contents of a menu of a limited number of conditions that can not only make up these hypotheses but also other alternative versions. It is the choice of these particular conditions that will really make the difference, to the scale and usability of the results of a QCA oriented evaluation. More optimistically, the search facility could even be made available online, for continued use by those interested in the evaluation results, and their possible variants

The Excel file for quick hypotheses testing is here: http://wp.me/afibj-1ux

In defense of the (careful) use of algorithms and the need for dialogue between tacit (expertise) and explicit (rules) forms of knowledge

This blog posting is a response to the following paper now available online

Greenhalgh, T., Howick, J., Maskrey, N., 2014. Evidence based medicine: a movement in crisis? BMJ 348, http://www.bmj.com/content/348/bmj.g3725

Background: Chris Roche passed this very interesting paper on to me, received via "Kate", who posted a comment on  Chris's posting on "What has cancer taught me about the links between medicine and development? which can be found on Duncan Green's "From Poverty to Power" blog. 

The paper is interesting in the first instance because both the debate and practice about evidence based policy and practice seems to be much further ahead in the field of medicine than it is in the field of development aid (...broad generalisation that this is...).

It is also of interest to reflect on the problems and solutions copied below and to think how many of these kinds of issues can also be seen in development aid programs.

 According to the paper, the problems with the current version of evidence based medicine include:

1. Distortion of the evidence based brand ("The first problem is that the evidence based “quality mark” has been misappropriated and distorted by vested interests. In particular, the drug and medical devices industries increasingly set the research agenda. They define what counts as disease ... They also decide which tests and treatments will  be compared in empirical studies and choose (often surrogate) outcome measures for establishing “efficacy.”
2. Too much evidence:  The second aspect of evidence based medicine’s crisis (and yet, ironically, also a measure of its success) is the sheer volume of evidence available. In particular, the number of clinical guidelines is now both unmanageable and unfathomable. One 2005 audit of a 24 hour medical take in an acute hospital, for example, included 18 patients with 44 diagnoses and identified 3679 pages of national guidelines (an estimated 122 hours ofreading) relevant to their immediate care"
3. Marginal gains and a shift from disease to risk: "Large trials designed to achieve marginal gains in a near saturated therapeutic field typically overestimate potential benefits (because trial samples are unrepresentative and, if the trial is overpowered, effects may be statistically but not clinically significant) and underestimate harms (because adverse events tend to be under detected or under reported)."
4. Overemphasis on following algorithmic rules: "Well intentioned efforts to automate use of evidence through computerised decision support systems, structured templates, and point of care prompts can crowd out the local,individualised, and patient initiated elements of the clinical consultation"
5. Poor fit for multi-morbidity. "Multi-morbidity (a single condition only in name) affects every person differently and seems to defy efforts to produce or apply objective scores, metrics, interventions, or guidelines"

The paper's proposed solutions or ways forward include:

1. Individualised for the patient: Real evidence based medicine has the care of individual patients as its top priority, asking, “what is the best course of action for this patient, in these circumstances, at this point in their illness or condition?” It consciously and reflexively refuses to let process (doing tests, prescribing medicines) dominate outcomes (the agreed goal of management in an individual case). 
2. Judgment not rules. Real evidence based medicine is not bound by rules.  
3. Aligned with professional, relationship based care.  Research evidence may still be key to making the right decision—but it does not determine that decision. Clinicians may provide information, but they are also trained to make ethical and technical judgments, and they hold a socially recognised role to care, comfort, and bear witness to suffering.
4. Public health dimension . Although we have focused on individual clinical care, there is also an important evidence base relating to population level interventions aimed at improving public health (such as pricing and labelling of consumables, fluoridation of water, and sex education). These are often complex, multifaceted programmes with important ethical and practical dimensions, but the same principles apply as in clinical care. 
5. Delivering real evidence based medicine. To deliver real evidence based medicine, the movement’s stakeholders must be proactive and persistent. Patients (for whose care the movement exists) must demand better evidence, better presented, better explained, and applied in a more personalised way with sensitivity to context and individual goals.
6. Training must be reoriented from rule following Critical appraisal skills—including basic numeracy, electronic database searching, and the ability systematically to ask questions of a research study—are prerequisites for competence in evidence based medicine. But clinicians need to be able to apply them to real case examples.
7. Evidence must be usable as well as robust. Another precondition for real evidence based medicine is that those who produce and summarise research evidence must attend more closely to the needs of those who might use it
8. Publishers must raise the bar. This raises an imperative for publishing standards. Just as journal editors shifted the expression of probability from potentially misleading P values to more meaningful confidence intervals by requiring them in publication standards, so they should now raise the bar for authors to improve the usability of evidence, and especially to require that research findings are presented in a way that informs individualised conversations.
9. ...and more

While many of these complaints and claims that make a lot of sense, I think there is also a risk"throwing the baby out with the bathwater" if care is not taken with some. I will focus on a couple of ideas that run through the paper.

The risk lies in seeing two alternative modes of practice as exclusive choices. One is rule based, focused on average affects when trying to meet common needs in populations and the other is expertise focused on the specific and often unique needs of individuals. Parallels could be drawn between different type of aid programs, e.g. centrally planned and nationally rolled out services meeting basic needs like water supply or education and much more person centered participatory rural development programs

Alternatively, one can see these two approaches as having complementary roles that can help and enrich each other. The authors describe one theory of learning which probably applies in many fields, including medicine: The first stage " ...beginning with the novice who learns the basic rules and applies them mechanically with no attention to context. The next two stages involve increasing depth of knowledge and sensitivity to context when applying rules. In the fourth and fifth stages, rule following gives way to expert judgments, characterised by rapid, intuitive reasoning informed by imagination, common sense, and judiciously selected research evidence and other rules"  During this process a lot of explicit knowledge become tacit, and almost automated, with conscious attention left for the more case specific features of a situation. It is an economic use of human cognitive powers. Michael Polanyi wrote about this process years ago (1966, The Tacit Dimension).

The other side of this process is when tacit knowledge gets converted into explicit knowledge. That's what some anthropologists and ethnographers do. They seek to get into the inner world of their subjects and to make it accessible to others. One practitioner whose work interests me in particular is Christina Gladwin, who wrote a book on Ethnographic Decision Trees in 1989. This was all about eliciting how people, like small farmers in west Africa, made decisions about what crops to plant. The result was a decision tree model, that summarised all the key choices farmers could make, and the final outcomes those different choices would lead to. This was not  a model of how they actually thought, but a model of how different combinations of choices were associated with different outcomes of interest. These decision trees are not so far removed from those used in medical practice today.

A new farmer coming into the same location could arguably make use of such a decision tree to decide what to crops to plant. Alternatively they could work with one of the farmers for a number of seasons, which then might cover all the eventualities in the decision tree, and learn from that direct experience. But this would take much more time. In this type of setting explicit rule based knowledge is an  easier and quicker means of transferring knowledge between people. Rule based knowledge that can be quickly and reliably communicated is also testable knowledge.  Following the same pattern of rules may or may not always lead to the expected outcome in another context.

And now a word about algorithms. An algorithm is a clearly defined sequence of steps that will lead to a desired end, sometimes involving some iteration until that end state gets closer. A sequence of choices in a decision tree is an algorithm. At each choice point the answer will dictate what choices to be made next. These are the rules mentioned in the paper above. There are also algorithms for constructing such algorithms. On this blog I have made a number of postings about QCA and (automated) Decision Tree models, both of which are means of constructing testable causal models. Both involve computerised processes for finding rules that best predict outcomes of interest. I think they have a lot of potential in the field of development aid.

But returning to the problems of evidence based medicine, it is very important to note that algorithms are means of achieving specific goals. Deciding which goals need to be pursued remains a very human choice. Even within the use of both QCA and (automated) Decision tree modeling users have to decide the extent to which they want to focus on finding rules that are very accurate or those which are less accurate but which apply to a wider range of circumstances (usually simple rather than complex rules).

So, in summary, in any move towards evidence based practice, we need to ensure that tacit and explicit forms of knowledge build upon each other rather than getting separated as different and competing forms of knowledge. And while we should develop, test and use good algorithms, we should remember they are always means to an end, and we remain responsible for choosing the ends we are trying to achieve.

Postscript 2015 05 04: Please also read this recent cautionary analysis of the use of algorithms for the purposes of public policy implementation. The author points out that algorithms can embody and perpetuate cultural biases. How is that possible? It is possible because all evidence-based algorithms are developed using historical data i.e. data sets of what has happened in the past. Those data sets, e.g. of arrest and conviction data in a given city reflect historical practice by human institutions in that city, with all their biases, conscious and not so conscious. They don't reflect ideal practice, simply the actual practice at the time. Where an algorithm is not based on analysis of historical data then it may have its origins in a more ethnographic study of the practice of human experts in the domain of interest. Their practice, and their interpretations of their practice, are also equally subject to cultural biases. The analysis by Virginia Eubanks include four useful suggestions to counter these risks, one of which is that "We need to learn more about how policy algorithms work" by demanding more transparency about the design of a given algorithm and its decisions. But this may not be possible, or in some cases publicly desirable. One alternative method of interest is the algorithmic audit.

A mistaken criticism of the value of binary data

When reviewing a recent evaluation report I came across the following comment:

"Crisp set QCA where binary codings are used to establish the presence or absence of certain conditions does not facilitate a nuanced or granular analysis."

(PS: Look here for the basics of QCA) 

Wrong. Simply wrong.

A DFID strategy for promoting "improved governance" could be coded  as present or absent. This does seem crude, given the varieties of ways in which a governance strategy could actually be implemented. But the answer is not to ditch binary coding, but to extend it.

This can be done by breaking down the concept of "a strategy for improved governance"  into a number of component parts or attributes, and then coding for their presence/absence. The initial conception of the governance strategy is then deemed present if all 10 attributes are present. But it only takes a single change in one attribute at a time to produce 9 new versions of almost the same strategy. If you change two attributes at a time, there are ( 1 think... 1-(10 x 10) =) 100 new versions. If any number of attributes can be changed then this means there are 2 to the power of 10 possible configurations of the strategy, some of which may be very different from the present strategy. Basically it does not take much tweaking of the initial configuration before you will have nuances by the bucketful!

The limitations of the dis-aggregation-into-components approach have nothing to do with the nature of binary coding, but rather whether there are enough cases available to allow identification of the kinds of outcomes associated with the different varieties of configurations arising from the more micro-level coding of attributes.

If there are enough cases available, then learning about what works through the emergence (or planned development) of variations in the initial configuration then becomes possible. Some of these new versions of a governance strategy may work more effectively than the initial model, and others less so. Incremental exploration becomes possible.

For more on the idea of exploring adjacent variations in causal configurations see Andreas Wagner's very interesting (2014) book titled "The Arrival of the Fittest" which explores a theory of how innovation is possible in biological systems. Here is a review of the book, in the Times Higher Education website.

There is also a connection here, I think, with Stuart Kauffman's concept of "the adjacent possible", an idea also taken up by Stephen Johnson in his book "Where do good ideas come from: The natural history of innovation" Here is a review of the book in the Guardian

Postscript 2015 05 14: I heard the same"binary is crude"  criticism again today from a person attending a QCA presentation at the UK Evaluation Society Conference in London.

This time I will present another response. Binary judgments can be and often are derived from a dichotomised scale that captures graduations of the phenomena of interest. As Carroll Patterson pointed out today, with current QCA software it is now possible to experiment with varying the location of the cut-off point on such scales, and observe the consequences for the quality of the configurations that are then identified as the best fitting solutions The same approach is also possible with searches for best-fitting configurations using an evolutionary algorithm, which is another approach I have been experimenting with recently. It is also possible to go much further into the specific details of the underlying concept being measured by a scale by basing it on the aggregated output of a weighted checklist, like the kind I have described elsewhere. Basically, the limit to what is possible is defined by the imagination of the researcher/evaluator, not any inherent limitation of binary measures.

Postscript  2015 05 17: I tried to post a Comment below in reply to Anon's comment below, but Blogger.com wont accept any HTML formatting, so I will place the comment here instead.

RE "If, to combat the reductiveness of binary coding, you introduce a scale of 4-6 points, you still face the same problem in coding something more complex – a remote non expert is reducing a complex context and process to a number in an arbitrary way. "

Coding for QCA (and other purposes, such as when using NVIVO) should always be done in a way that is transparent and replicable, with attention to inter-rate reliability. It should certainly not be done in an “arbitrary way”

RE "Grading a large, diverse and complicated country on a scale of 0-1 or 1-5 on 'improved governance' is just ridiculous. Anyone who has studied the way people actually behave, governance, how decisions are really made or projects succeed or fail, will tell you that this reductiveness does not helpfully or accurately reflect reality."

QCA has been used in a field of Political Science since the 1980s and many of these applications have been cross-country analyses of political systems.

RE QCA is not qualitative – as it seeks to reduce a complex qualitative issue to a quantitative score - a number.

In crisp-set QCA data set the “number” 0 or 1 is actually a category not a numerical value. QCA could be done just as well by replacing  the 0’s and 1’s with the words “absent” and “present” 

RE "QCA is not comparative – the serious comparative part comes afterwards in some form of qualitative analysis, which researchers can choose. Looking at the truth table for patterns is the only form of comparison that QCA offers."

There are two levels of analysis involved in QCA: within-case analysis and between-case analysis.  At the beginning within-case analysis informs the selection of conditions to be included in a data set. When inconsistencies are found in an examination of configurations in a data set good practice advises a return to within-case analysis to identify missing conditions that can resolve these inconsistencies.  When these have been resolved and set of configurations has been identified that accounts for all case in the most parsimonious way possible,  these then need to be interpreted by reference to   the details of specific cases, with particular attention to more detailed process that connect the conditions making up the configurations.

RE "In my view QCA is a quantitative form of data management and pattern identification."

It does depend on what you mean by quantitative. It is based on a form of mathematics known as set theory, but that is about logical relationships, not quantities. In case there is any reservation about its significance, pattern identification is very important. In a data set with 10 different conditions there are 2 to the 10 different possible combinations of these that might be consistently associated with an outcome of interest. Finding these is like looking for a needle in a haystack. QCA and other methods like decision tree algorithms, help us find what part of the haystack the needle is most likely to be found. But as I said at the end of my section of the UKES presentation, finding a plausible configuration is not enough. It is necessary but not sufficient  for a strong causal claim. There also needs to be a plausible account of the likely causal mechanisms at work that connect the conditions in the configurations. These will only be found and confirmed through detailed within-case investigations, using methods like (but not only) process tracing. And the pattern finding has to be systematic, and transparent in the way it has been done. This is the case with QCA and Decision Tree modeling, where there are specifics algorithms used, both with their known limitations

There is a useful reference that may be of interest: Wagemann, C., Schneider, C.Q., 2007. Standards of Good Practice in Qualitative Comparative Analysis (qca) and Fuzzy-Sets. http://www.compasss.org/wpseries/WagemannSchneider2007.pdf