Upcoming Meeting on spring 2025

TimeProgram
14:15-15:00Rickard Karlsson (TU Delft) - Falsification of Unconfoundedness in Multi-Environment Data by Testing Independence of Causal Mechanisms

A major challenge in estimating treatment effects in observational studies is the reliance on untestable conditions, such as the assumption of no unmeasured confounding. In this talk, we discuss a novel falsification strategy for testing the assumption of no unmeasured confounding in a setting with observational data from multiple heterogeneous sources, which we refer to as environments. Our proposed falsification strategy leverages the key observation that unmeasured confounding can create spurious correlations between causal mechanisms across environments. Building on this observation, we develop a procedure that detects these dependencies with high statistical power while controlling false positives. The algorithm does not require access to randomized data and, in contrast to other falsification approaches, functions even under transportability violations when the environment has a direct effect on the outcome of interest. To showcase the practical relevance of our approach, we demonstrate that our method efficiently detects confounding in both simulated and semi-synthetic real-world data.
15:15-16:00Sourbh Bhadane (UvA) - Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypotheses

Reasoning about fairness through correlation-based notions is rife with pitfalls. The 1973 University of California, Berkeley graduate school admissions case from Bickel et al. (1975) is a classic example of one such pitfall, namely Simpson’s paradox. In this talk we reason about the Berkeley graduate school admissions case through a causal lens. We compare different causal notions of fairness that are based on graphical, counterfactual and interventional queries on the causal model, and develop statistical tests for these notions that use only observational data. We study the logical relations between notions, and show that while notions may not be equivalent, their corresponding statistical tests coincide for the case at hand. In particular, we introduce a statistical test for causal hypothesis testing based on Pearl’s instrumental-variable inequalities (Pearl, 1995) and discuss the implications on concluding about fairness through the resulting statistical test.
16:00-17:00Drinks