Average treatment effect;
Endogeneity;
Instrumental variables;
Union of moment inequalities;
Bootstrap;
Uniform validity;
Multiple testing;
Familywise error rate;
Gatekeeping;
TREATMENT EFFECT BOUNDS;
IDENTIFICATION;
MONOTONICITY;
EQUATIONS;
D O I:
10.1016/j.jeconom.2018.04.007
中图分类号:
F [经济];
学科分类号:
02 ;
摘要:
This paper considers identification and inference about the sign of the average effect of a binary endogenous regressor (or treatment) on a binary outcome of interest when a binary instrument is available. In this setting, the average effect of the endogenous regressor on the outcome is sometimes referred to as the average treatment effect (ATE). We consider four different sets of assumptions: instrument exogeneity, instrument exogeneity and monotonicity on the outcome equation, instrument exogeneity and monotonicity on the equation for the endogenous regressor, or instrument exogeneity and monotonicity on both the outcome equation and the equation for the endogenous regressor. For each of these sets of conditions, we characterize when (i) the distribution of the observed data is inconsistent with the assumptions and (ii) the distribution of the observed data is consistent with the assumptions and the sign of ATE is identified. A distinguishing feature of our results is that they are stated in terms of a reduced form parameter from the population regression of the outcome on the instrument. In particular, we find that the reduced form parameter being far enough, but not too far, from zero, implies that the distribution of the observed data is consistent with our assumptions and the sign of ATE is identified, while the reduced form parameter being too far from zero implies that the distribution of the observed data is inconsistent with our assumptions. For each set of restrictions, we also develop methods for simultaneous inference about the consistency of the distribution of the observed data with our restrictions and the sign of the ATE when the distribution of the observed data is consistent with our restrictions. We show that our inference procedures are valid uniformly over a large class of possible distributions for the observed data that include distributions where the instrument is arbitrarily "weak." A novel feature of the methodology is that the null hypotheses involve unions of moment inequalities. (C) 2019 Elsevier B.V. All rights reserved.
机构:
Erasmus MC, Dept Epidemiol, POB 2040, NL-3000 CA Rotterdam, Netherlands
Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Boston, MA USAErasmus MC, Dept Epidemiol, POB 2040, NL-3000 CA Rotterdam, Netherlands
Swanson, Sonja A.
Hernan, Miguel A.
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h-index: 0
机构:
Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Boston, MA USA
Harvard TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA USA
Harvard Mit Div Hlth Sci & Technol, Boston, MA USAErasmus MC, Dept Epidemiol, POB 2040, NL-3000 CA Rotterdam, Netherlands
Hernan, Miguel A.
论文数: 引用数:
h-index:
机构:
Miller, Matthew
Robins, James M.
论文数: 0引用数: 0
h-index: 0
机构:
Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Boston, MA USA
Harvard TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA USAErasmus MC, Dept Epidemiol, POB 2040, NL-3000 CA Rotterdam, Netherlands
Robins, James M.
Richardson, Thomas S.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Washington, Dept Stat, Seattle, WA 98195 USAErasmus MC, Dept Epidemiol, POB 2040, NL-3000 CA Rotterdam, Netherlands
机构:
Department of Statistics and Data Science, National University of Singapore, SingaporeDepartment of Statistics and Data Science, National University of Singapore, Singapore
Sun, Baoluo
Cui, Yifan
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h-index: 0
机构:
Center for Data Science Zhejiang University, ChinaDepartment of Statistics and Data Science, National University of Singapore, Singapore
Cui, Yifan
Tchetgen, Eric Tchetgen
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h-index: 0
机构:
Department of Statistics and Data Science The Wharton School, University of Pennsylvania, United StatesDepartment of Statistics and Data Science, National University of Singapore, Singapore