Simulating counterfactuals
This work addresses a methodological challenge in counterfactual inference for fairness analysis, representing an incremental advancement.
The paper tackles the problem of simulating counterfactual distributions when conditions are set on discrete and continuous variables, presenting an algorithm that functions as a particle filter for asymptotically valid inference, and applies it to fairness analysis in credit-scoring.
Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world. If the evidence specifies a conditional distribution on a manifold, counterfactuals may be analytically intractable. We present an algorithm for simulating values from a counterfactual distribution where conditions can be set on both discrete and continuous variables. We show that the proposed algorithm can be presented as a particle filter leading to asymptotically valid inference. The algorithm is applied to fairness analysis in credit-scoring.