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Estimating dynamic causal effects with unobserved confounders: a latent class version of the inverse probability weighted estimator
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In this article, 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, is considered.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.read more
Citations
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Identification and estimation of average causal effects when treatment status is ignorable within unobserved strata
TL;DR: This paper extended matching and propensity score reweighting methods to settings in which unobserved variables influence both treatment assignment and counterfactual outcomes, and proposed a method to reweight the matching scores in such settings.
Journal ArticleDOI
Evaluating sensitivity to classification uncertainty in latent subgroup effect analyses
Wen Wei Loh,Jee-Seon Kim +1 more
TL;DR: In this article , the authors proposed a method for assessing the sensitivity of inference to classification uncertainty when using such classify-analyze approaches for subgroup effect analyses by exploiting each individual's typically nonzero predictive or posterior subgroup membership probabilities to gauge the stability of the resultant subgroup-specific average causal effects estimates over different, carefully selected subsets of the individuals.
References
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Bradley Efron,Robert Tibshirani +1 more
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Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
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