Exogenous Matching: Learning Good Proposals for Tractable Counterfactual Estimation
This addresses efficient counterfactual estimation for causal inference applications, representing an incremental improvement over existing importance sampling techniques.
The authors tackled the problem of tractable counterfactual estimation by proposing Exogenous Matching, an importance sampling method that minimizes variance through conditional distribution learning, demonstrating outperformance over existing methods in experiments with Structural Causal Models.
We propose an importance sampling method for tractable and efficient estimation of counterfactual expressions in general settings, named Exogenous Matching. By minimizing a common upper bound of counterfactual estimators, we transform the variance minimization problem into a conditional distribution learning problem, enabling its integration with existing conditional distribution modeling approaches. We validate the theoretical results through experiments under various types and settings of Structural Causal Models (SCMs) and demonstrate the outperformance on counterfactual estimation tasks compared to other existing importance sampling methods. We also explore the impact of injecting structural prior knowledge (counterfactual Markov boundaries) on the results. Finally, we apply this method to identifiable proxy SCMs and demonstrate the unbiasedness of the estimates, empirically illustrating the applicability of the method to practical scenarios.