TY - JOUR
T1 - Heterogeneous Treatment Effects in the Presence of Self-Selection
T2 - A Propensity Score Perspective
AU - Zhou, Xiang
AU - Xie, Yu
N1 - Funding Information:
The authors benefited from communications with Daniel Almirall, Matthew Blackwell, Jennie Brand, James Heckman, Jeffrey Smith, and Edward Vytlacil. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Grant No. R01-HD-074603-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© American Sociological Association 2019.
PY - 2020/8/1
Y1 - 2020/8/1
N2 - An essential feature common to all empirical social research is variability across units of analysis. Individuals differ not only in background characteristics but also in how they respond to a particular treatment, intervention, or stimulation. Moreover, individuals may self-select into treatment on the basis of anticipated treatment effects. To study heterogeneous treatment effects in the presence of self-selection, Heckman and Vytlacil developed a structural approach that builds on the marginal treatment effect (MTE). In this article, we extend the MTE-based approach through a redefinition of MTE. Specifically, we redefine MTE as the expected treatment effect conditional on the propensity score (rather than all observed covariates) as well as a latent variable representing unobserved resistance to treatment. As with the original MTE, the new MTE also can be used as a building block for evaluating standard causal estimands. However, the weights associated with the new MTE are simpler, more intuitive, and easier to compute. Moreover, the new MTE is a bivariate function and thus is easier to visualize than the original MTE. Finally, the redefined MTE immediately reveals treatment-effect heterogeneity among individuals who are at the margin of treatment. As a result, it can be used to evaluate a wide range of policy changes with little analytical twist and design policy interventions that optimize the marginal benefits of treatment. We illustrate the proposed method by estimating heterogeneous economic returns to college with National Longitudinal Study of Youth 1979 data.
AB - An essential feature common to all empirical social research is variability across units of analysis. Individuals differ not only in background characteristics but also in how they respond to a particular treatment, intervention, or stimulation. Moreover, individuals may self-select into treatment on the basis of anticipated treatment effects. To study heterogeneous treatment effects in the presence of self-selection, Heckman and Vytlacil developed a structural approach that builds on the marginal treatment effect (MTE). In this article, we extend the MTE-based approach through a redefinition of MTE. Specifically, we redefine MTE as the expected treatment effect conditional on the propensity score (rather than all observed covariates) as well as a latent variable representing unobserved resistance to treatment. As with the original MTE, the new MTE also can be used as a building block for evaluating standard causal estimands. However, the weights associated with the new MTE are simpler, more intuitive, and easier to compute. Moreover, the new MTE is a bivariate function and thus is easier to visualize than the original MTE. Finally, the redefined MTE immediately reveals treatment-effect heterogeneity among individuals who are at the margin of treatment. As a result, it can be used to evaluate a wide range of policy changes with little analytical twist and design policy interventions that optimize the marginal benefits of treatment. We illustrate the proposed method by estimating heterogeneous economic returns to college with National Longitudinal Study of Youth 1979 data.
KW - causal effects
KW - heterogeneity
KW - instrumental variable
KW - marginal treatment effect
KW - propensity score
KW - selection bias
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U2 - 10.1177/0081175019862593
DO - 10.1177/0081175019862593
M3 - Article
C2 - 34121778
AN - SCOPUS:85067218283
SN - 0081-1750
VL - 50
SP - 350
EP - 385
JO - Sociological Methodology
JF - Sociological Methodology
IS - 1
ER -