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Abstract

We 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.



Item Type: MPRA Paper -

Original Title: Estimating dynamic causal effects with unobserved confounders: a latent class version of the inverse probability weighted estimator-

Language: English-

Keywords: Causal inference, Longitudinal design, Mixture model, Potential outcomes, Sequential treatment-

Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and SelectionH - Public Economics > H2 - Taxation, Subsidies, and Revenue > H25 - Business Taxes and SubsidiesC - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models-





Author: Bartolucci, Francesco

Source: https://mpra.ub.uni-muenchen.de/43430/







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