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Register a new userIdentifiability of causal effects with multiple causes and a binary outcome
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Linbo Wang
Publication date
2019
fields
Mathematical Statistics
and research's language is
English
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Unobserved confounding presents a major threat to causal inference from observational studies. Recently, several authors suggest that this problem may be overcome in a shared confounding setting where multiple treatments are independent given a common latent confounder. It has been shown that under a linear Gaussian model for the treatments, the causal effect is not identifiable without parametric assumptions on the outcome model. In this paper, we show that the causal effect is indeed identifiable if we assume a general binary choice model for the outcome with a non-probit link. Our identification approach is based on the incongruence between Gaussianity of the treatments and latent confounder, and non-Gaussianity of a latent outcome variable. We further develop a two-step likelihood-based estimation procedure.
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Unobserved confounding is a major hurdle for causal inference from observational data. Confounders---the variables that affect both the causes and the outcome---induce spurious non-causal correlations between the two. Wang & Blei (2018) lower this hurdle with the blessings of multiple causes, where the correlation structure of multiple causes provides indirect evidence for unobserved confounding. They leverage these blessings with an algorithm, called the deconfounder, that uses probabilistic factor models to correct for the confounders. In this paper, we take a causal graphical view of the deconfounder. In a graph that encodes shared confounding, we show how the multiplicity of causes can help identify intervention distributions. We then justify the deconfounder, showing that it makes valid inferences of the intervention. Finally, we expand the class of graphs, and its theory, to those that include other confounders and selection variables. Our results expand the theory in Wang & Blei (2018), justify the deconfounder for causal graphs, and extend the settings where it can be used.
Although the exposure can be randomly assigned in studies of mediation effects, any form of direct intervention on the mediator is often infeasible. As a result, unmeasured mediator-outcome confounding can seldom be ruled out. We propose semiparametric identification of natural direct and indirect effects in the presence of unmeasured mediator-outcome confounding by leveraging heteroskedasticity restrictions on the observed data law. For inference, we develop semiparametric estimators that remain consistent under partial misspecifications of the observed data model. We illustrate the proposed estimators through both simulations and an application to evaluate the effect of self-efficacy on fatigue among health care workers during the COVID-19 outbreak.
(This comment has been updated to respond to Wang and Bleis rejoinder [arXiv:1910.07320].) The premise of the deconfounder method proposed in Blessings of Multiple Causes by Wang and Blei [arXiv:1805.06826], namely that a variable that renders multiple causes conditionally independent also controls for unmeasured multi-cause confounding, is incorrect. This can be seen by noting that no fact about the observed data alone can be informative about ignorability, since ignorability is compatible with any observed data distribution. Methods to control for unmeasured confounding may be valid with additional assumptions in specific settings, but they cannot, in general, provide a checkable approach to causal inference, and they do not, in general, require weaker assumptions than the assumptions that are commonly used for causal inference. While this is outside the scope of this comment, we note that much recent work on applying ideas from latent variable modeling to causal inference problems suffers from similar issues.
Causal inference has been increasingly reliant on observational studies with rich covariate information. To build tractable causal models, including the propensity score models, it is imperative to first extract important features from high dimensional data. Unlike the familiar task of variable selection for prediction modeling, our feature selection procedure aims to control for confounding while maintaining efficiency in the resulting causal effect estimate. Previous empirical studies imply that one should aim to include all predictors of the outcome, rather than the treatment, in the propensity score model. In this paper, we formalize this intuition through rigorous proofs, and propose the causal ball screening for selecting these variables from modern ultra-high dimensional data sets. A distinctive feature of our proposal is that we do not require any modeling on the outcome regression, thus providing robustness against misspecification of the functional form or violation of smoothness conditions. Our theoretical analyses show that the proposed procedure enjoys a number of oracle properties including model selection consistency, normality and efficiency. Synthetic and real data analyses show that our proposal performs favorably with existing methods in a range of realistic settings.
Instrumental variable methods are widely used for inferring the causal effect of an exposure on an outcome when the observed relationship is potentially affected by unmeasured confounders. Existing instrumental variable methods for nonlinear outcome models require stringent identifiability conditions. We develop a robust causal inference framework for nonlinear outcome models, which relaxes the conventional identifiability conditions. We adopt a flexible semi-parametric potential outcome model and propose new identifiability conditions for identifying the model parameters and causal effects. We devise a novel three-step inference procedure for the conditional average treatment effect and establish the asymptotic normality of the proposed point estimator. We construct confidence intervals for the causal effect by the bootstrap method. The proposed method is demonstrated in a large set of simulation studies and is applied to study the causal effects of lipid levels on whether the glucose level is normal or high over a mice dataset.
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