Here’s the announcement, and here’s the video:

Better Than Difference in Differences

Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University

It is not always clear how to adjust for control data in causal inference, balancing the goals of reducing bias and variance. We show how, in a setting with repeated experiments, Bayesian hierarchical modeling yields an adaptive procedure that uses the data to determine how much adjustment to perform. The result is a novel analysis with increased statistical efficiency compared with the default analysis based on difference estimates. The increased efficiency can have real-world consequences in terms of the conclusions that can be drawn from the experiments. An open question is how to apply these ideas in the context of a single experiment or observational study, in which case the optimal adjustment cannot be estimated from the data; still, the principle holds that difference-in-differences can be extremely wasteful of data.

The talk follows up on Andrew Gelman and Matthijs Vákár (2021), Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data, Statistics in Medicine 40, 3403-3424, https://www.stat.columbia.edu/~gelman/research/published/chickens.pdf

Here’s the talk I gave in this seminar a few years ago:

100 Stories of Causal Inference

In social science we learn from stories. The best stories are anomalous and immutable (see https://www.stat.columbia.edu/~gelman/research/published/storytelling.pdf). We shall briefly discuss the theory of stories, the paradoxical nature of how we learn from them, and how this relates to forward and reverse causal inference. Then we will go through some stories of applied causal inference and see what lessons we can draw from them. We hope this talk will be useful as a model for how you can better learn from own experiences as participants and consumers of causal inference.

No overlap, I think.