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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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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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.
doaj +1 more source
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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Evaluating Boolean relationships in Configurational Comparative Methods [PDF]
Configurational Comparative Methods (CCMs) aim to learn causal structures from datasets by exploiting Boolean sufficiency and necessity relationships. One important challenge for these methods is that such Boolean relationships are often not satisfied in
De Souter, Luna
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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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Bias of the additive hazard model in the presence of causal effect heterogeneity [PDF]
Hazard ratios are prone to selection bias, compromising their use as causal estimands. On the other hand, if Aalen’s additive hazard model applies, the hazard difference has been shown to remain unaffected by the selection of frailty factors over time ...
Post, Richard A.J. +2 more
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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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