Causal inference

Book on causal models for qualititative and mixed methods research

Alan Jacobs and I have been working on figuring out principles for simultaneously drawing inferences from qualitative and quantitative data. The key idea is that when scholars use qualitative inference they update beliefs about causal effects (or more, generally about their model of the world, \(M\)) by making inferences using data about many facts of a given case (\(D_1\)). They estimate a posterior \(\Pr(M \vert D_1)\). Quantitative scholars update beliefs about causal effects by making inferences using data about a few facts about many cases (\(D_2\)), forming posterior \(\Pr(M \mid D_2)\). From there it’s not such a huge thing to make integrated inferences of the form \(\Pr(M \vert D_1\&D_2)\).

Simple as that sounds, people do not do this, but doing it opens up many insights about how we learn from cases and how we aggregate knowledge. The broad approach becomes considerably more powerful when causal models are used to justify beliefs on data patterns.

Alan and I develop these ideas in our book Integrated inferences (with Alan Jacobs, Cambridge University) and provide a package CausalQueries to implement these ideas. (See also our guide to our package for making, updating, and querying causal models).

Pedagogical material

Short working papers

  1. Why a Bayesian researcher might prefer observational data
    Macartan Humphreys
  2. Bounds on least squares estimates of causal effects in the presence of heterogeneous assignment probabilities
    Macartan Humphreys