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Doubly Robust Estimation of Causal Effects in Strategic Equilibrium Systems

arXiv 2510.15555 ↗First posted 17 Oct 2025cs.LGcited by 0

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Abstract (arXiv)

We introduce the Strategic Doubly Robust (SDR) estimator, a novel framework that integrates strategic equilibrium modeling with doubly robust estimation for causal inference in strategic environments. SDR addresses endogenous treatment assignment arising from strategic agent behavior, maintaining double robustness while incorporating strategic considerations. Theoretical analysis confirms SDR's consistency and asymptotic normality under strategic unconfoundedness. Empirical evaluations demonstrate SDR's superior performance over baseline methods, achieving 7.6%-29.3% bias reduction across varying strategic strengths and maintaining robust scalability with agent populations. The framework provides a principled approach for reliable causal inference when agents respond strategically to interventions.

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References · 4

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  2. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  3. 2015Strategic Classificationcited by 257
  4. 2016Double/Debiased Machine Learning for Treatment and Causal Parameterscited by 43

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