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  5. Econometrics for Policy Analysis
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Econometrics for Policy Analysis

Paper Session

Saturday, Jan. 6, 2024 2:30 PM - 4:30 PM (CST)

Convention Center, 301C
Hosted By: Korea-America Economic Association
  • Chair: Soonwoo Kwon, Brown University

Individualized Treatment Allocation in Sequential Network Game

Toru Kitagawa
,
Brown University
Guanyi Wang
,
University College London

Abstract

Designing individualized allocation of treatments so as to maximize the equilibrium welfare of interacting agents has many policy-relevant applications.Focusing on sequential decision games of interacting agents, this paper develops a method to obtain optimal treatment assignment rules that maximize a social welfare criterion by evaluating stationary distributions of outcomes. Stationary distributions in sequential decision games are given by Gibbs distributions, which are difficult to optimize with respect to a treatment allocation due to analytical and computational complexity. We apply a variational approximation to the stationary distribution and optimize the approximated equilibrium welfare with respect to treatment allocation using a greedy optimization algorithm. We characterize the performance of the variational approximation, deriving a performance guarantee for the greedy optimization algorithm via a welfare regret bound. We establish the convergence rate of this bound. We implement our proposed method in simulation exercises and an empirical application using the Indian microfinance data (Benerjee et al 2013), and show it delivers significant welfare gains.

Assessing Heterogeneity of Treatment Effects

Tetsuya Kaji
,
University of Chicago
Jianfei Cao
,
Northeastern University

Abstract

Treatment heterogeneity is an important issue in economics, but its assessment is hindered by the fundamental lack of identification of the joint distribution of treated and control outcomes. For example, we may want to assess the effect of insurance on the health of otherwise unhealthy individuals, but it is infeasible to insure only the unhealthy ones, so the causal effects for those are not identified. Or, we may be interested in the shares of winners and losers from a minimum wage increase, but without observing the counterfactual, the winners and losers are not identified. In this paper, we derive bounds on these quantities that complement quantile treatment effects. Applications illustrate that our bounds provide useful insights on heterogeneity even when the average treatment effects are not significant.

How to Sample and When to Stop Sampling: The Generalized Wald Problem and Minimax Policies

Karun Adusumilli
,
University of Pennsylvania

Abstract

Acquiring information is expensive. Experimenters need to carefully choose how many units of each treatment to sample and when to stop sampling. The aim of this paper is to develop techniques for incorporating the cost of information into experimental design. In particular, we study sequential experiments where sampling is costly and a decision-maker aims to determine the best treatment for full scale implementation by (1) adaptively allocating units to two possible treatments, and (2) stopping the experiment when the expected welfare (inclusive of sampling costs) from implementing the chosen treatment is maximized. Working under the diffusion limit, we describe the optimal policies under the minimax regret criterion. Under small cost asymptotics, the same policies are also optimal under parametric and non-parametric distributions of outcomes. The minimax optimal sampling rule is just the Neyman allocation; it is independent of sampling costs and does not adapt to previous outcomes. The decision-maker stops sampling when the average difference between the treatment outcomes, multiplied by the number of observations collected until that point, exceeds a specific threshold. We also suggest methods for inference on the treatment effects using stopping times and discuss their optimality.

Causal Inference from Hypothetical Evaluations

Michael Pollmann
,
Duke University
B. Douglas Bernheim
,
Stanford University
Daniel Björkegren
,
Brown University
Jeffrey Naecker
,
Google

Abstract

This paper develops a method that learns the relationship between hypothetical responses and real choices in observational data, and then uses that estimated relationship to predict the effect of counterfactuals. After developing the econometric theory for the estimator, we demonstrate that it can be applied in settings where standard methods are not applicable. In both a lab and field setting we show it can recover accurate estimates of treatment effects that are close to ground truth experimental estimates.
JEL Classifications
  • C1 - Econometric and Statistical Methods and Methodology: General
  • C5 - Econometric Modeling