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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Testing for treatment effect twice using internal and external controls in clinical trials
Journal of Causal Inference, 2023 Leveraging external controls – relevant individual patient data under control from external trials or real-world data – has the potential to reduce the cost of randomized controlled trials (RCTs) while increasing the proportion of trial patients given ...
Yi Yanyao, Zhang Ying, Du Yu, Ye Ting
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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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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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Estimating causal effects with the neural autoregressive density estimator
Journal of Causal Inference, 2021 The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional
Garrido Sergio +3 more
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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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On the bias of adjusting for a non-differentially mismeasured discrete confounder
Journal of Causal Inference, 2021 Biological and epidemiological phenomena are often measured with error or imperfectly captured in data. When the true state of this imperfect measure is a confounder of an outcome exposure relationship of interest, it was previously widely believed that ...
Peña Jose M. +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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