2-for-1 Week: Sebastian Lemire on Causation Coding

My name is Sebastian. Before pursuing my PhD at UCLA, I served as a senior evaluation consultant at Ramboll Management – a Copenhagen-based consulting firm. My current interests revolve around research syntheses and causal modeling techniques.

A common practice in evaluation is to examine the existing body of evidence of the type of intervention to be evaluated. The most well established approach is perhaps the generic literature review, often provided as a setting-the-scene segment in evaluation reports. The purpose of today’s tip is to push for a more interpretive approach when coding findings from existing evaluations.

The approach – called causation coding – is grounded in qualitative data analysis. In the words of Saldaña (2013), causation coding is appropriate for discerning motives (by or toward something or someone), belief systems, worldviews, processes, recent histories, interrelationships, and the complexity of influences and affects on human actions and phenomena (p.165).

In its practical application, causation coding aims to map out causal chains (CODE1 > CODE2 > CODE3), corresponding to a delivery mechanism, an outcome, and a mediator linking the delivery mechanism and outcome (ibid). These types of causal triplets are often made available in evaluation reports, as authors explain how and why the evaluated intervention generated change.

In a recent review of M4P Market development programs, I employed causation coding to capture causally relevant information in 13 existing evaluations and to develop hypotheses about how and why these programs generate positive outcomes. The latter informed the evaluation of a similar market development program.

Lessons Learned:

(1) It is important to award careful attention to the at times conflated distinction between empirically supported and hypothetically predicted causal chains. The latter express how the author(s) intended the program to work. In many evaluation studies, the eagerness to predict the success of the intervention often contributes to the inclusion of these hypothetical scenarios in results sections. Attention should be awarded the empirically supported causal chains.

(2) Causal chains are rarely summarized in a three-part sequence from cause(s) to mechanism(s) to outcome(s). As such, causation coding often involves a high degree of sensitivity to words such as “because”, “in effect”, “therefore” and “since” that might indicate an underlying causal logic (ibid).

Rad Resource: The coding manual for qualitative researchers (second edition) by Saldaña.

We’re celebrating 2-for-1 Week here at aea365. With tremendous interest in the blog lately, we’ve had many authors eager to share their evaluation wisdom, so for one special week, readers will be treated to two blog posts per day! Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to aea365@eval.org . aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.


1 thought on “2-for-1 Week: Sebastian Lemire on Causation Coding”

  1. Dear Sebastian.

    This sounds really interesting and I am interested to learn more about the idea of causal modelling. In particular, I would be interested to learn how it addresses the context sensitivity of market systems development programmes that seems to limit our ability to learn / generalise from one programme and apply this learning in a different context – in particular when it comes to causal mechanisms.

    I work for a knowledge platform on all things market development including M4P (the BEAM Exchange). We also work on how to effectively evaluate these initiatives and the described approach would be a welcome addition to our catalogue of methods. Please get in touch.

    Kind regards, Marcus

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