Simulating counterfactuals

• URL: http://arxiv.org/abs/2306.15328v3
• Date: Tue, 26 Mar 2024 18:19:54 GMT
• Title: Simulating counterfactuals
• Authors: Juha Karvanen, Santtu Tikka, Matti Vihola,
• Abstract summary: Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world. We present an algorithm for simulating values from a counterfactual distribution where conditions can be set on both discrete and continuous variables.
• Score: 1.3654846342364302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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