Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
Many practical problems involve estimating low dimensional statistical quantities with high-
dimensional models and datasets. Several approaches address these estimation tasks …
dimensional models and datasets. Several approaches address these estimation tasks …
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 real-world data (RWD) are available. We …
randomized control trial (RCT) data and real-world data (RWD) are available. We …
A double machine learning approach to combining experimental and observational data
Experimental and observational studies often lack validity due to untestable assumptions.
We propose a double machine learning approach to combine experimental and …
We propose a double machine learning approach to combine experimental and …
Many Data: Combine Experimental and Observational Data through a Power Likelihood
Randomized controlled trials are commonly regarded as the gold standard for causal
inference and play a pivotal role in modern evidence-based medicine. However, the sample …
inference and play a pivotal role in modern evidence-based medicine. However, the sample …
Understanding the risks and rewards of combining unbiased and possibly biased estimators, with applications to causal inference
Several problems in statistics involve the combination of high-variance unbiased estimators
with low-variance estimators that are only unbiased under strong assumptions. A notable …
with low-variance estimators that are only unbiased under strong assumptions. A notable …
Enhancing Statistical Validity and Power in Hybrid Controlled Trials: A Randomization Inference Approach with Conformal Selective Borrowing
Randomized controlled trials (RCTs) are the gold standard for causal inference but may lack
power because of small populations in rare diseases and limited participation in common …
power because of small populations in rare diseases and limited participation in common …
Case study of semaglutide and cardiovascular outcomes: An application of the Causal Roadmap to a hybrid design for augmenting an RCT control arm with real-world …
Introduction: Increasing interest in real-world evidence has fueled the development of study
designs incorporating real-world data (RWD). Using the Causal Roadmap, we specify three …
designs incorporating real-world data (RWD). Using the Causal Roadmap, we specify three …
Efficient combination of observational and experimental datasets under general restrictions on outcome mean functions
HH Li - arXiv preprint arXiv:2406.06941, 2024 - arxiv.org
A researcher collecting data from a randomized controlled trial (RCT) often has access to an
auxiliary observational dataset that may be confounded or otherwise biased for estimating …
auxiliary observational dataset that may be confounded or otherwise biased for estimating …
A Causal Roadmap for Hybrid Randomized and Real-World Data Designs: Case Study of Semaglutide and Cardiovascular Outcomes
LE Dang, E Fong, JM Tarp, KKB Clemmensen… - arXiv preprint arXiv …, 2023 - arxiv.org
Introduction: Increasing interest in real-world evidence has fueled the development of study
designs incorporating real-world data (RWD). Using the Causal Roadmap, we specify three …
designs incorporating real-world data (RWD). Using the Causal Roadmap, we specify three …
Covariate Adjustment in Modern Causal Design and Analysis
LD Liao - 2024 - search.proquest.com
Clinicians, policymakers, psychologists, and economists often ask causal questions: does
this treatment or intervention cause the observed differences in outcome? If researchers can …
this treatment or intervention cause the observed differences in outcome? If researchers can …