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A Bayesian Semiparametric Approach to Treatment Effect Variation with Noncompliance

Jared Fisher (BYU) joins us to discuss estimating varying treatment effects in randomized trials with noncompliance is inherently challenging since variation comes from two separate sources: variation in the impact itself and variation in the compliance rate. In this setting, existing Frequentist and ML-based methods are quite flexible but are highly sensitive to the so-called […]

Confidence Sets for Causal Orderings

Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal model or a single equivalence class of models, less attention has been given to quantifying uncertainty in causal discovery in terms of confidence statements. The primary challenge in causal discovery […]

A graph-theoretic approach for testing causal effects under interference.

David Puelz from UT Austin and the Salem Center presents an approach for randomization tests of causal effects under general forms of interference.  The key idea is to represent a null hypothesis of spillovers as a bipartite graph and condition the test on a biclique in this graph. The approach is completely algorithmic and is […]