The Bayesian Causal Effect Estimation Algorithm

Talbot, Denis; Lefebvre, Geneviève; Atherton, Juli

doi:10.1515/jci-2014-0035

Cited by 18 publications

(28 citation statements)

References 26 publications

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“…The CovSel package offers two algorithms for determining the minimal confounding set, which, we label deLunaA1 (algorithm 1) and deLunaA2 (algorithm 2) (Häggström et al, ). Talbot et al () build on Bayesian variable selection approaches proposed by Wang et al () by formally incorporating the outcome model into their variable selection method. We used the function ABCEE, from the R package, BCEE, developed by the authors (Talbot et al, ) and use all author‐recommended defaults (Talbot et al, ).…”

Section: Illustrating the Outcome‐adaptive Lassomentioning

confidence: 99%

“…Zigler and Dominici () propose a Bayesian approach for selecting variables as well as averaging over several possible PS models that may include different sets of covariates. The problem of variable selection in causal inference is also discussed in Robins and Greenland (), Judkins et al (), Schneeweiss et al (), Vansteelandt et al (), Van der Laan and Gruber (), Rolling and Yang (), and Talbot et al ().…”

Section: Introductionmentioning

confidence: 99%

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Outcome-Adaptive Lasso: Variable Selection for Causal Inference

2017

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Summary Methodological advancements, including propensity score methods, have resulted in improved unbiased estimation of treatment effects from observational data. Traditionally, a “throw in the kitchen sink” approach has been used to select covariates for inclusion into the propensity score, but recent work shows including unnecessary covariates can impact both the bias and statistical efficiency of propensity score estimators. In particular, the inclusion of covariates that impact exposure but not the outcome, can inflate standard errors without improving bias, while the inclusion of covariates associated with the outcome but unrelated to exposure can improve precision. We propose the outcome-adaptive lasso for selecting appropriate covariates for inclusion in propensity score models to account for confounding bias and maintaining statistical efficiency. This proposed approach can perform variable selection in the presence of a large number of spurious covariates, i.e. covariates unrelated to outcome or exposure. We present theoretical and simulation results indicating that the outcome-adaptive lasso selects the propensity score model that includes all true confounders and predictors of outcome, while excluding other covariates. We illustrate covariate selection using the outcome-adaptive lasso, including comparison to alternative approaches, using simulated data and in a survey of patients using opioid therapy to manage chronic pain.

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Section: Illustrating the Outcome‐adaptive Lassomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Outcome-Adaptive Lasso: Variable Selection for Causal Inference

2017

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“…() proposed a Bayesian model averaging procedure that uses an informative prior to place more weight a priori on outcome models that include covariates associated with the exposure. Many ideas have built on this prior specification to address the issue of confounder selection and model uncertainty (Talbot et al., ; Wang et al, ; Cefalu et al, ). There also exists a small literature on dimension‐preserving statistics that can be used in a similar manner as propensity scores to balance confounders between levels of a binary treatment.…”

Section: Introductionmentioning

confidence: 99%

Doubly Robust Matching Estimators for High Dimensional Confounding Adjustment

et al. 2018

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Valid estimation of treatment effects from observational data requires proper control of confounding. If the number of covariates is large relative to the number of observations, then controlling for all available covariates is infeasible. In cases where a sparsity condition holds, variable selection or penalization can reduce the dimension of the covariate space in a manner that allows for valid estimation of treatment effects. In this article, we propose matching on both the estimated propensity score and the estimated prognostic scores when the number of covariates is large relative to the number of observations. We derive asymptotic results for the matching estimator and show that it is doubly robust in the sense that only one of the two score models need be correct to obtain a consistent estimator. We show via simulation its effectiveness in controlling for confounding and highlight its potential to address nonlinear confounding. Finally, we apply the proposed procedure to analyze the effect of gender on prescription opioid use using insurance claims data.

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“…Among others, we did not consider methods that combine two or more approaches, such as the combination of the disjunctive cause criterion with model-free backward selection (VanderWeele & Shpitser, 2011), combinations of causal diagrams and CIE (Evans, Chaix, Lobbedez, Verger, & Flahault, 2012;Weng, Hsueh, Messam, & Hertz-Picciotto, 2009), or the adjustment uncertainty algorithm by Crainiceanu, Dominici, and Parmigiani (2008) combining outcome-and treatment-oriented selection with the CIE criterion. Also, we did not consider model averaging approaches as described in Wang, Parmigiani, and Dominici (2012), Zigler and Dominici (2014), and Talbot, Lefebvre, and Atherton (2015).…”

Section: Discussionmentioning

confidence: 99%

Covariate selection strategies for causal inference: Classification and comparison

Witte

Didelez

2018

Biometrical J

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When causal effects are to be estimated from observational data, we have to adjust for confounding. A central aim of covariate selection for causal inference is therefore to determine a set that is sufficient for confounding adjustment, but other aims such as efficiency or robustness can be important as well. In this paper, we review six general approaches to covariate selection that differ in the targeted type of adjustment set.We discuss and illustrate their advantages and disadvantages using causal diagrams. Moreover, the approaches and different ways of implementing them are compared empirically in an extensive simulation study. We conclude that there are considerable differences between the approaches but none of them is uniformly best, with performance depending on the chosen adjustment method as well as the true confounding structure. Any prior structural knowledge on the causal relations is helpful to choose the most appropriate method. K E Y W O R D Saverage causal effect, causal diagram, confounder selection, propensity score matching, variable selection 1270

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The Bayesian Causal Effect Estimation Algorithm

Cited by 18 publications

References 26 publications

Outcome-Adaptive Lasso: Variable Selection for Causal Inference

Outcome-Adaptive Lasso: Variable Selection for Causal Inference

Doubly Robust Matching Estimators for High Dimensional Confounding Adjustment

Covariate selection strategies for causal inference: Classification and comparison

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