🤖 AI Summary
In strategic equilibrium environments, agents’ strategic behaviors induce endogenous treatment assignment, posing challenges for causal inference. This paper proposes the Strategic Doubly Robust (SDR) estimation framework, which integrates game-theoretic strategic equilibrium modeling into the doubly robust estimation paradigm. Under a “strategic ignorability” assumption, SDR simultaneously addresses strategic unobserved confounding and model misspecification risks, preserving consistency, asymptotic normality, and doubly robust protection. Theoretical analysis establishes its statistical reliability under strategic confounding; empirical evaluations demonstrate that SDR reduces estimation bias by 7.6%–29.3% across varying strategic intensities relative to baseline methods, while maintaining scalability with increasing agent populations. The core contribution is the first systematic incorporation of strategic equilibrium structure—rooted in game theory—into the doubly robust causal inference framework, thereby bridging strategic interaction modeling with robust causal estimation.
📝 Abstract
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.