A new three-step method for using inverse propensity weighting with latent class analysis [PDF]
, 2022 Bias-adjusted three-step latent class analysis (LCA) is widely popular to relate covariates to class membership. However, if the causal effect of a treatment on class membership is of interest and only observational data is available, causal inference ...
Clouth, F. J. +3 more
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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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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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Examination of the 1970 National Bureau of Standards Underground Corrosion Test Welded Stainless STeel Coupons from Site D [PDF]
, 2008 A 1970 study initiated by the National Bureau of Standards (NBS), now known as the National Institute of Standards and Technology (NIST), buried over 6000 corrosion coupons or specimens of stainless steel Types 201, 202, 301, 304, 316, 409, 410, 430, and
Flitton, M. K. Adler +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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Robust smoothing of left-censored time series data with a dynamic linear model to infer SARS-CoV-2 RNA concentrations in wastewater [PDF]
, 2023 Wastewater sampling for the detection and monitoring of SARS-CoV-2 has been developed and applied at an unprecedented pace, however uncertainty remains when interpreting the measured viral RNA signals and their spatiotemporal variation. The proliferation
Chris J. Lilley +9 more
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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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Toric Ideals of Characteristic Imsets via Quasi-Independence Gluing
, 2022 Characteristic imsets are 0-1 vectors which correspond to Markov equivalence classes of directed acyclic graphs. The study of their convex hull, named the characteristic imset polytope, has led to new and interesting geometric perspectives on the ...
Hollering, Benjamin +3 more
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, 2023 Inferring the effect of interventions within complex systems is a fundamental problem of statistics. A widely studied approach employs structural causal models that postulate noisy functional relations among a set of interacting variables. The underlying
Drton, Mathias, Strieder, David
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