Multiple Treatments with Strategic Interaction

Abstract

We develop an empirical framework in which we identify and estimate the effects of treatments on outcomes of interest when the treatments are the result of strategic interaction (e.g., bargaining, oligopolistic entry, peer effects). We consider a model where agents play a discrete game with complete information whose equilibrium actions (i.e., binary treatments) determine a post-game outcome in a nonseparable model with endogeneity. Due to the simultaneity in the first stage, the model as a whole is incomplete and the selection process fails to exhibit the conventional monotonicity. Without imposing parametric restrictions or large support assumptions, this poses challenges in recovering treatment parameters. To address these challenges, we first analytically characterize regions that predict equilibria in the first-stage game with possibly more than two players, and ascertain a monotonic pattern of these regions. Based on this finding, we derive bounds on the average treatment effects (ATE's) under nonparametric shape restrictions and the existence of excluded exogenous variables. We also introduce and point identify a multi-treatment version of local average treatment effects (LATE's). We apply our method to data on airlines and air pollution in cities in the U.S. We find that (i) the causal effect of each airline on pollution is positive, and (ii) the effect is increasing in the number of firms but at a decreasing rate.