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.
JOSÉ M Pena
exaly +2 more sources
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.
Arvid Sjölander
exaly +2 more sources
Simple yet sharp sensitivity analysis for unmeasured confounding
Journal of Causal Inference, 2022 We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains
Peña Jose M.
doaj +1 more source
Estimating marginal treatment effects under unobserved group heterogeneity
Journal of Causal Inference, 2022 This article studies the treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment rules. By using a finite mixture approach, we propose a marginal treatment effect (MTE) framework in which the ...
Hoshino Tadao, Yanagi Takahide
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Conditional average treatment effect estimation with marginally constrained models
Journal of Causal Inference, 2023 Treatment effect estimates are often available from randomized controlled trials as a single average treatment effect for a certain patient population.
van Amsterdam Wouter A. C. +1 more
doaj +1 more source
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
doaj +1 more source
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.
doaj +1 more source
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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