B0471
Title: Causal aggregation: Estimation and inference of causal effects by constraint-based data fusion
Authors: Jaime Gimenez - Stanford University (United States)
Dominik Rothenhaeusler - Stanford University (United States) [presenting]
Abstract: Randomized experiments are the gold standard for causal inference. In experiments, usually, one variable is manipulated, and its effect is measured on an outcome. However, practitioners may also be interested in the effect of simultaneous interventions on multiple covariates on a fixed target variable. We discuss a method that allows estimating the effect of joint interventions using data from different experiments in which only very few variables are manipulated. The proposed method allows combining data sets arising from randomized experiments and observational data sets for which IV assumptions or unconfoundedness hold. Compared to existing approaches, the approach is applicable in settings with very little knowledge about the graph. This makes the approach potentially more reliable in cases where the practitioner has limited background knowledge. We demonstrate the effectiveness of the proposed method on synthetic and semi-synthetic data.
