This study develops identification and estimation methods for treatment effect models with strategic interaction in  treatment decisions. We consider models where one's treatment choice and outcome can be endogenously affected by others' treatment choices. We formulate the interaction of  treatment decisions as a two-player complete information game with potential multiple equilibria. For this model, under the assumption of a stochastic equilibrium selection rule, we prove that the marginal treatment effect (MTE) from one's own treatment and that from his/her partner's can be separately point-identified using a latent index framework . Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the MTE parameters using series approximation. We show that the proposed estimator is uniformly consistent with the optimal convergence rate and has asymptotic normality .
We develop identification and estimation methods for treatment effect models with strategic interaction in the treatment decisions. We consider models where one's treatment choice and outcome can be endogenously affected by others' treatment choices. We formulate the interaction of the treatment decisions as a two-player complete information game with potential multiple equilibria. For this model, using a latent index framework and the assumption of a stochastic equilibrium selection rule, we prove that the marginal treatment effect from one's own treatment and that from his/her partner's can be separately point-identified with potential over-identifiability . Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the marginal treatment effects using series approximation. The proposed estimator is shown to be uniformly consistent and asymptotically normal. As an empirical illustration, we investigate the impacts of risky behaviors on adolescents' academic performance .
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This study develops
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the
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the
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under
using a latent index framework and
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(MTE)
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using a latent index framework
with potential over-identifiability
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MTE parameters
marginal treatment effects
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We show that the
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uniformly consistent with the optimal convergence rate and has asymptotic normality
shown to be uniformly consistent and asymptotically normal. As an empirical illustration, we investigate the impacts of risky behaviors on adolescents' academic performance