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Causal inference with recurrent and competing events
Many research questions concern treatment effects on outcomes that can recur several times in the same individual. For example, medical researchers...
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Functional Causal Inference with Time-to-Event Data
Functional data analysis has proven to be a powerful tool for capturing and analyzing complex patterns and relationships in a variety of fields,...
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Causal Inference with Secondary Outcomes
In this paper, we develop new methods for identifying causal effects in the presence of unmeasured confounding with continuous treatment and outcome....
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CHEMIST: an R package for causal inference with high-dimensional error-prone covariates and misclassified treatments
In this paper, we study causal inference with complex and noisy data accommodated. A new structure is called CHEMIST, which refers to Causal...
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Special issue on Advanced statistical modeling and causal inference with complex data for better decision making
The special issue on Advanced Statistical Modeling and Causal Inference with Complex data for Better Decision Making has been inspired by the...
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Third moment-based causal inference
In observational data, covariance-based measures of dependence are of limited use for detecting reverse-causation (using y → x instead of x → y when...
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Targeted maximum likelihood estimation for causal inference in survival and competing risks analysis
Targeted maximum likelihood estimation (TMLE) provides a general methodology for estimation of causal parameters in presence of high-dimensional...
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Examples of Applying Causal-Inference Roadmap to Real-World Studies
The causal-inference roadmap described in Chapter 8 consists of six key steps to derive real-world evidence (RWE) from the analysis of real-world...
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A nonparametric binomial likelihood approach for causal inference in instrumental variable models
Instrumental variable methods allow for inference about the treatment effect by controlling for unmeasured confounding in randomized experiments with...
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Statistical Challenges for Causal Inference Using Time-to-Event Real-World Data
Real-world data (RWD) have been increasingly used in drug development, e.g., for indirect comparisons of treatments in real-world settings and...
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Causal Inference: Efficacy and Mechanism Evaluation
In randomized trials, the primary analysis is usually based on an intention-to-treat approach which answers the question “What is the effect of...
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Bayesian Framework for Causal Inference with Principal Stratification and Clusters
In observational studies, principal stratification is a well-established method in causal analysis to adjust the treatment effect estimation for...
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Causal Inference with Targeted Learning for Producing and Evaluating Real-World Evidence
Targeted Learning (TL) provides a unified framework for generating and evaluating real-world evidence (RWE) and thus can serve as a foundation for...
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A semiparametric multiply robust multiple imputation method for causal inference
Evaluating the impact of non-randomized treatment on various health outcomes is difficult in observational studies because of the presence of...
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Text-Based Causal Inference on Irony and Sarcasm Detection
The state-of-the-art NLP models’ success advanced significantly as their complexity increased in recent years. However, these models tend to consider...
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A Resampling Approach for Causal Inference on Novel Two-Point Time-Series with Application to Identify Risk Factors for Type-2 Diabetes and Cardiovascular Disease
Two-point time-series data, characterized by baseline and follow-up observations, are frequently encountered in health research. We study a novel...
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Is Fisher inference inferior to Neyman inference for policy analysis?
The increasing computational power has led to an increasing interest in Fisher’s test in social science. As the Fisher and Neyman inference are based...
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The Designed Bootstrap for Causal Inference in Big Observational Data
The combination of modern machine learning algorithms with the nonparametric bootstrap can enable effective predictions and inferences on Big...
