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 […]

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 […]

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 […]