Robust variance estimation and inference for causal effect estimation
Journal of Causal Inference, 2023 We present two novel approaches to variance estimation of semi-parametric efficient point estimators of the treatment-specific mean: (i) a robust approach that directly targets the variance of the influence function (IF) as a counterfactual mean outcome ...
Tran Linh +3 more
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Attributable fraction and related measures: Conceptual relations in the counterfactual framework
Journal of Causal Inference, 2023 The attributable fraction (population) has attracted much attention from a theoretical perspective and has been used extensively to assess the impact of potential health interventions. However, despite its extensive use, there is much confusion about its
Suzuki Etsuji, Yamamoto Eiji
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Bounding the probabilities of benefit and harm through sensitivity parameters and proxies
Journal of Causal Inference, 2023 We present two methods for bounding the probabilities of benefit (a.k.a. the probability of necessity and sufficiency, i.e., the desired effect occurs if and only if exposed) and harm (i.e., the undesired effect occurs if and only if exposed) under ...
Peña Jose M.
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When is a Match Sufficient? A Score-based Balance Metric for the Synthetic Control Method
Journal of Causal Inference, 2020 In some applications, researchers using the synthetic control method (SCM) to evaluate the effect of a policy may struggle to determine whether they have identified a “good match” between the control group and treated group. In this paper, we demonstrate
Parast Layla +3 more
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On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder
Journal of Causal Inference, 2020 Suppose that we are interested in the average causal effect of a binary treatment on an outcome when this relationship is confounded by a binary confounder. Suppose that the confounder is unobserved but a nondifferential proxy of it is observed.
Peña Jose M.
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A note on a sensitivity analysis for unmeasured confounding, and the related E-value
Journal of Causal Inference, 2020 Unmeasured confounding is one of the most important threats to the validity of observational studies. In this paper we scrutinize a recently proposed sensitivity analysis for unmeasured confounding.
Sjölander Arvid
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Averaging causal estimators in high dimensions
Journal of Causal Inference, 2020 There has been increasing interest in recent years in the development of approaches to estimate causal effects when the number of potential confounders is prohibitively large.
Antonelli Joseph, Cefalu Matthew
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From urn models to box models: Making Neyman's (1923) insights accessible
Journal of Causal InferenceNeyman’s 1923 paper introduced the potential outcomes framework and the foundations of randomization-based inference. We discuss the influence of Neyman’s paper on four introductory to intermediate-level textbooks by Berkeley faculty members (Scheffé ...
Lin Winston +3 more
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Conditional generative adversarial networks for individualized causal mediation analysis
Journal of Causal InferenceMost classical methods popularly used in causal mediation analysis can only estimate the average causal effects and are difficult to apply to precision medicine.
Huan Cheng, Sun Rongqian, Song Xinyuan
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Prospective and retrospective causal inferences based on the potential outcome framework
Journal of Causal InferenceIn this article, we discuss both prospective and retrospective causal inferences, building on Neyman’s potential outcome framework. For prospective causal inference, we review criteria for confounders and surrogates to avoid the Yule–Simpson paradox and ...
Geng Zhi +4 more
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