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Treatment Effects with Unobserved Heterogeneity: A Set Identification Approach

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We propose the sharp identifiable bounds of the distribution functions of potential outcomes using a panel with fixed T. We allow for the possibility that the statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the bounds. Dynamics in the treatment decisions is allowed as long as the stationarity assumptions are satisfied. In particular, we present an example where our assumptions are satisfied and the treatment decision of the present time may depend on the treatments and the observed outcomes of the past. As an empirical illustration we study the effect of smoking during pregnancy on infant birth weights. We found that for the group of switchers the birth weight with smoking is first order stochastically dominated by that with non-smoking.

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  • Yoonseok Lee & Sung Jae Jun & Youngki Shin, 2014. "Treatment Effects with Unobserved Heterogeneity: A Set Identification Approach," Center for Policy Research Working Papers 169, Center for Policy Research, Maxwell School, Syracuse University.
    • Handle: RePEc:max:cprwps:169
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    Cited by:

    1. Brantly Callaway & Tong Li, 2019. "Quantile treatment effects in difference in differences models with panel data," Quantitative Economics, Econometric Society, vol. 10(4), pages 1579-1618, November.
    2. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2016. "Identification of panel data models with endogenous censoring," Journal of Econometrics, Elsevier, vol. 194(1), pages 57-75.
    3. Pablo Lavado & Gonzalo Rivera, 2016. "Identifying Treatment Effects with Data Combination and Unobserved Heterogeneity," Working Papers 79, Peruvian Economic Association.
    4. Shosei Sakaguchi, 2017. "Estimation of Average Treatment Effects Using Panel Data when Treatment Effects Are Heterogeneous by Unobserved Fixed Effects," KIER Working Papers 970, Kyoto University, Institute of Economic Research.
    5. Pablo Lavado, "undated". "Identifying Treatment Effects and Counterfactual Distributions using Data Combination with Unobserved Heterogeneity," Working Papers 13-25, Departamento de Economía, Universidad del Pacífico.
    6. Pablo Lavado & Gonzalo Rivera, 2015. "Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity," Working Papers 15-14, Centro de Investigación, Universidad del Pacífico.

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    More about this item

    Keywords

    Treatment Effects; Dynamic Treatment Decisions; Partial Identification; Unobserved Heterogeneity; Stochastic Dominance; Panel Data;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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