Estimating dynamic causal effects with unobserved confounders: a latent class version of the inverse probability weighted estimator
AbstractWe consider estimation of the causal effect of a sequential binary treatment (typically corresponding to a policy or a subsidy in the economic context) on a final outcome, when the treatment assignment at a given occasion depends on the sequence of previous assignments as well as on time-varying confounders. In this case, a popular modeling strategy is represented by Marginal Structural Models; within this approach, the causal effect of the treatment is estimated by the Inverse Probability Weighting (IPW) estimator, which is consistent provided that all the confounders are observed (sequential ignorability). To alleviate this serious limitation, we propose a new estimator, called Latent Class Inverse Probability Weighting (LC-IPW), which is based on two steps: first, a finite mixture model is fitted in order to compute latent-class-specific weights; then, these weights are used to fit the Marginal Structural Model of interest. A simulation study shows that the LC-IPW estimator outperforms the IPW estimator for all the considered configurations, even in cases of no unobserved confounding. The proposed approach is applied to the estimation of the causal effect of wage subsidies on employment, using a dataset of Finnish firms observed for eight years. The LC-IPW estimate confirms the existence of a positive effect, but its magnitude is nearly halved with respect to the IPW estimate, pointing out the substantial role of unobserved confounding in this setting.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 43430.
Date of creation: 08 Oct 2012
Date of revision:
Causal inference; Longitudinal design; Mixture model; Potential outcomes; Sequential treatment;
Find related papers by JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- H25 - Public Economics - - Taxation, Subsidies, and Revenue - - - Business Taxes and Subsidies
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
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