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Journal of Causal Inference
Scopus
ISSN 2193-3685 · De Gruyter · 272 articles in the index
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Journal Article
Generalized coarsened confounding for causal effects: a large-sample framework
There has been widespread use of causal inference methods for the rigorous analysis of observational studies and to identify policy evaluations. In this article, we consider a class of generalized coarsened procedures for confounding. At a high level, these procedures can be viewed as performing a…
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Journal Article
Discovery of critical thresholds in mixed exposures and estimation of policy intervention effects
Regulations of chemical exposures often focus on individual substances, neglecting the amplified toxicity that can arise from multiple concurrent exposures. We propose a novel methodology to identify critical thresholds in multivariate exposure spaces and estimate the effects of policy…
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Journal Article
A causal framework for the self-controlled case series design
Vaccine safety surveillance programs that monitor possible short-term rare adverse events following vaccination usually only have access to data on vaccinated individuals who experienced the event of interest. The Self-Controlled Case Series design employs such data and compares the risk of the…
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Journal Article
Semiparametric discovery and estimation of interaction in mixed exposures using stochastic interventions
Understanding the complex interactions among multiple environmental exposures is critical for assessing their combined impact on health outcomes. This study introduces InterXshift, a novel semiparametric method that provides a nonparametric definition of interaction and facilitates both the…
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Journal Article
Bridging binarization: causal inference with dichotomized continuous exposures
The average treatment effect (ATE) is a common parameter estimated in causal inference literature, but it is only defined for binary exposures. Thus, despite concerns raised by some researchers, many studies seeking to estimate the causal effect of a continuous exposure create a new binary exposure…
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Journal Article
A diagnostic to find and help combat stochastic positivity issues – with a focus on continuous treatments
The positivity assumption is central in the identification of a causal effect. Especially its stochastic variant is an issue many applied researchers face. Yet positivity is rarely discussed, especially in conjunction with continuous treatments or Modified Treatment Policies. One common…
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Journal Article
Adaptive-TMLE for the average treatment effect based on randomized controlled trial augmented with real-world data
We consider the problem of estimating the average treatment effect (ATE) when both randomized control trial (RCT) data and external real-world data (RWD) are available. We decompose the ATE estimand as the difference between a pooled-ATE estimand that integrates RCT and RWD and a bias estimand that…
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Journal Article
Assessing surrogate heterogeneity in real world data using meta-learners
Surrogate markers are most commonly studied within the context of randomized clinical trials. However, the need for alternative outcomes also extends to real-world public health and social science research, where randomized trials are often impractical. While standard methods for evaluating…
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Journal Article
Estimating average causal effects with incomplete exposure and confounders
Standard methods for estimating average causal effects require complete observations of the exposure and confounders. In observational studies, however, missing data are ubiquitous. Motivated by a study on the effect of prescription opioids on mortality, we propose methods for estimating average…
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Journal Article
Neymanian inference in randomized experiments
In his seminal 1923 work, Neyman studied the variance estimation problem for the difference-in-means estimator of the average treatment effect in completely randomized experiments. He proposed a variance estimator that is conservative in general and unbiased under homogeneous treatment effects…
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Journal Article
The optimality of blocking designs in equally and unequally allocated randomized experiments with general response
We consider the performance of the difference-in-means estimator in a two-arm randomized experiment under common experimental endpoints such as continuous (regression), incidence, proportion and survival. We examine performance under both equal and unequal allocation to treatment groups and we…
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Journal Article
Doubly-robust functional average treatment effect estimation
Understanding causal relationships in the presence of complex, structured data remains a central challenge in modern statistics and science in general. While traditional causal inference methods are well-suited for scalar outcomes, many scientific applications demand tools capable of handling…
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Journal Article
Do LLMs act as repositories of causal knowledge?
Large language models (LLMs) offer the potential to automate a large number of tasks that previously have not been possible to automate, including some in science. There is considerable interest in whether LLMs can automate the process of causal inference by providing the information about causal…
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Journal Article
Estimating the effects of staggered interventions with count and binary outcomes: a simulation study
Difference-in-Differences and Event-Study regression estimators are commonly used to estimate treatment effects. However, when effects are heterogeneous, the standard two-way fixed effects (TWFE) regression can provide biased estimates. Recent literature proposes alternative estimators to overcome…
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Journal Article
A note on the properties of the confidence set for the local average treatment effect obtained by inverting the score test
We study the properties of the score confidence set for the local average treatment effect in nonparametric and semiparametric instrumental variable models. This confidence set is constructed by inverting a score test based on an estimate of the nonparametric influence function for the estimand…
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Journal Article
The observational partial order of causal structures with latent variables
For two causal structures with the same set of visible variables, one is said to observationally dominate the other if the set of distributions over the visible variables realizable by the first contains the set of distributions over the visible variables realizable by the second. Knowing such…
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Journal Article
Doubly robust estimation of log-linear structural nested mean models for binary outcomes using coherent nuisance models
Time-varying treatments can be confounded by time-varying prognostic factors influenced by earlier treatments. Structural nested mean models (SNMMs) adjust for time-varying confounders by parameterizing causal effects conditioned on variable history, and their parameters can be estimated using…
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Journal Article
HAL-based plug-in estimation with pointwise asymptotic normality of the causal dose–response curve
Estimating and obtaining reliable inference for the marginally adjusted causal dose–response curve for continuous treatments without relying on parametric assumptions is a well-known statistical challenge. Parametric models risk introducing significant bias through model misspecification…
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Journal Article
Unfair utility functions and first steps towards improving them
Many fairness criteria constrain the policy or choice of predictors, which can have unwanted consequences, in particular, when optimizing the policy under such constraints. Here, we instead suggest that fairness can be directly analyzed as a property of the utility function. Instead of imposing…
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Journal Article
Untangling sample and population level estimands in Bayesian causal computation
Model-based Bayesian inference for sample and population-level causal estimands has been growing in popularity. This literature routinely emphasizes clear specification of the target estimand, however blind implementation of standard computational procedures may implicitly target estimands that…