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Randomized Controlled Trials (RCTs) represent a gold standard when developing policy guidelines. However, RCTs are often narrow, and lack data on broader populations of interest. Causal effects in these populations are often estimated using…

Machine Learning · Computer Science 2023-03-07 Zeshan Hussain , Michael Oberst , Ming-Chieh Shih , David Sontag

Confounding is a significant obstacle to unbiased estimation of causal effects from observational data. For settings with high-dimensional covariates -- such as text data, genomics, or the behavioral social sciences -- researchers have…

Artificial Intelligence · Computer Science 2024-02-01 Katherine A. Keith , Sergey Feldman , David Jurgens , Jonathan Bragg , Rohit Bhattacharya

Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experimental data from a randomized controlled trial (RCT). A major…

Randomized controlled trials (RCTs) are increasingly prevalent in education research, and are often regarded as a gold standard of causal inference. Two main virtues of randomized experiments are that they (1) do not suffer from…

With increasing data availability, causal effects can be evaluated across different data sets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the effect of the treatment from that of unwanted (confounding)…

Methods that infer causal dependence from observational data are central to many areas of science, including medicine, economics, and the social sciences. A variety of theoretical properties of these methods have been proven, but empirical…

Methodology · Statistics 2021-07-08 Amanda Gentzel , Purva Pruthi , David Jensen

Randomized Controlled Trials (RCT) are the current gold standards to empirically measure the effect of a new drug. However, they may be of limited size and resorting to complementary non-randomized data, referred to as observational, is…

Methodology · Statistics 2025-06-11 Ahmed Boughdiri , Julie Josse , Erwan Scornet

Randomized Controlled Trials (RCTs) are pivotal in generating internally valid estimates with minimal assumptions, serving as a cornerstone for researchers dedicated to advancing causal inference methods. However, extending these findings…

Methodology · Statistics 2024-05-28 Melody Y Huang , Harsh Parikh

Bridging the gap between internal and external validity is crucial for heterogeneous treatment effect estimation. Randomised controlled trials (RCTs), favoured for their internal validity due to randomisation, often encounter challenges in…

Causal inference is vital for informed decision-making across fields such as biomedical research and social sciences. Randomized controlled trials (RCTs) are considered the gold standard for internal validity of inferences, whereas…

Methodology · Statistics 2025-12-02 Ruoqi Yu , Bikram Karmakar , Jessica Vandeleest , Eleanor Bimla Schwarz

Randomized controlled trials (RCTs) often include subgroup analyses to assess whether treatment effects vary across pre-specified patient populations. However, these analyses frequently suffer from small sample sizes which limit the power…

Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore observational studies based on passively observed data are widely accepted as an…

Artificial Intelligence · Computer Science 2016-11-11 Jiuyong Li , Thuc Duy Le , Lin Liu , Jixue Liu , Zhou Jin , Bingyu Sun , Saisai Ma

Online experiments such as Randomised Controlled Trials (RCTs) or A/B-tests are the bread and butter of modern platforms on the web. They are conducted continuously to allow platforms to estimate the causal effect of replacing system…

Machine Learning · Computer Science 2023-04-24 Olivier Jeunen

One of the major challenges in estimating conditional potential outcomes and conditional average treatment effects (CATE) is the presence of hidden confounders. Since testing for hidden confounders cannot be accomplished only with…

Machine Learning · Computer Science 2025-06-17 Ahmed Aloui , Juncheng Dong , Ali Hasan , Vahid Tarokh

Observational studies provide the only evidence on the effectiveness of interventions when randomized controlled trials (RCTs) are impractical due to cost, ethical concerns, or time constraints. While many methodologies aim to draw causal…

We develop new methods to integrate experimental and observational data in causal inference. While randomized controlled trials offer strong internal validity, they are often costly and therefore limited in sample size. Observational data,…

Econometrics · Economics 2025-11-04 Xuelin Yang , Licong Lin , Susan Athey , Michael I. Jordan , Guido W. Imbens

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…

Methodology · Statistics 2022-07-13 Yanyao Yi , Ying Zhang , Yu Du , Ting Ye

Despite their cost, randomized controlled trials (RCTs) are widely regarded as gold-standard evidence in disciplines ranging from social science to medicine. In recent decades, researchers have increasingly sought to reduce the resource…

Methodology · Statistics 2026-02-24 Guilherme Duarte

We extend Fisher's randomization test (FRT) to test conditional independence between observed outcomes and treatments given covariates in both randomized experiments and observational studies, with no restriction on the variable type of…

Methodology · Statistics 2025-06-12 Zhen Zhong

Disruptions in clinical trials may be due to external events like pandemics, warfare, and natural disasters. Resulting complications may lead to unforeseen intercurrent events (events that occur after treatment initiation and affect the…

Applications · Statistics 2024-08-20 Rachael V. Phillips , Mark J. van der Laan
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