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تسجيل مستخدم جديدCausal Inference for Nonlinear Outcome Models with Possibly Invalid Instrumental Variables
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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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Instrumental variable methods provide a powerful approach to estimating causal effects in the presence of unobserved confounding. But a key challenge when applying them is the reliance on untestable exclusion assumptions that rule out any relationshi
p between the instrument variable and the response that is not mediated by the treatment. In this paper, we show how to perform consistent IV estimation despite violations of the exclusion assumption. In particular, we show that when one has multiple candidate instruments, only a majority of these candidates---or, more generally, the modal candidate-response relationship---needs to be valid to estimate the causal effect. Our approach uses an estimate of the modal prediction from an ensemble of instrumental variable estimators. The technique is simple to apply and is black-box in the sense that it may be used with any instrumental variable estimator as long as the treatment effect is identified for each valid instrument independently. As such, it is compatible with recent machine-learning based estimators that allow for the estimation of conditional average treatment effects (CATE) on complex, high dimensional data. Experimentally, we achieve accurate estimates of conditional average treatment effects using an ensemble of deep network-based estimators, including on a challenging simulated Mendelian Randomization problem.
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 dimension
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Consider the problem of estimating the local average treatment effect with an instrument variable, where the instrument unconfoundedness holds after adjusting for a set of measured covariates. Several unknown functions of the covariates need to be es
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Instrumental variable methods are among the most commonly used causal inference approaches to account for unmeasured confounders in observational studies. The presence of invalid instruments is a major concern for practical applications and a fast-gr
owing area of research is inference for the causal effect with possibly invalid instruments. The existing inference methods rely on correctly separating valid and invalid instruments in a data dependent way. In this paper, we illustrate post-selection problems of these existing methods. We construct uniformly valid confidence intervals for the causal effect, which are robust to the mistakes in separating valid and invalid instruments. Our proposal is to search for the causal effect such that a sufficient amount of candidate instruments can be taken as valid. We further devise a novel sampling method, which, together with searching, lead to a more precise confidence interval. Our proposed searching and sampling confidence intervals are shown to be uniformly valid under the finite-sample majority and plurality rules. We compare our proposed methods with existing inference methods over a large set of simulation studies and apply them to study the effect of the triglyceride level on the glucose level over a mouse data set.
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