# Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesis

arXiv: https://arxiv.org/abs/2511.19735 · HTML: https://mlsanity.com/paper/2511.19735/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 113 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Builds on (most cited first, 29 of 29)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations](https://mlsanity.com/paper/2002.04083/) (2020)
- [Conformal Inference of Counterfactuals and Individual Treatment Effects](https://mlsanity.com/paper/2006.06138/) (2020)
- [Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approach](https://mlsanity.com/paper/2111.12161/) (2021)
- [Causal inference methods for combining randomized trials and observational studies: a review](https://mlsanity.com/paper/2011.08047/) (2020)
- [Long-term Causal Inference Under Persistent Confounding via Data Combination](https://mlsanity.com/paper/2202.07234/) (2022)
- [Elastic Integrative Analysis of Randomized Trial and Real-World Data for Treatment Heterogeneity Estimation](https://mlsanity.com/paper/2005.10579/) (2020)
- [Data thinning for convolution-closed distributions](https://mlsanity.com/paper/2301.07276/) (2023)
- [Data fusion methods for the heterogeneity of treatment effect and confounding function](https://mlsanity.com/paper/2007.12922/) (2020)
- [Multiply robust estimation of causal effects under principal ignorability](https://mlsanity.com/paper/2012.01615/) (2020)
- [Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey](https://mlsanity.com/paper/2501.18934/) (2025)
- [Improving trial generalizability using observational studies](https://mlsanity.com/paper/2003.01242/) (2020)
- [Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population](https://mlsanity.com/paper/2201.06595/) (2022)
- [Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data](https://mlsanity.com/paper/2405.07186/) (2024)
- [Double Machine Learning Methods for Estimating Average Treatment Effects: A Comparative Study](https://mlsanity.com/paper/2204.10969/) (2022)
- [A Causal Roadmap for Generating High-Quality Real-World Evidence](https://mlsanity.com/paper/2305.06850/) (2023)
- [Enhancing Treatment Effect Estimation: A Model Robust Approach Integrating Randomized Experiments and External Controls using the Double Penalty Integration Estimator](https://mlsanity.com/paper/2307.04304/) (2023)
- [Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalization](https://mlsanity.com/paper/2412.08869/) (2024)
- [Pretest estimation in combining probability and non-probability samples](https://mlsanity.com/paper/2305.17801/) (2023)
- [Semiparametric Localized Principal Stratification Analysis with Continuous Strata](https://mlsanity.com/paper/2406.13478/) (2024)
- [Improving randomized controlled trial analysis via data-adaptive borrowing](https://mlsanity.com/paper/2306.16642/) (2023)
- [Harmonized Estimation of Subgroup-Specific Treatment Effects in Randomized Trials: The Use of External Control Data](https://mlsanity.com/paper/2308.05073/) (2023)
- [Enhancing Statistical Validity and Power in Hybrid Controlled Trials: A Randomization Inference Approach with Conformal Selective Borrowing](https://mlsanity.com/paper/2410.11713/) (2024)
- [Doubly protected estimation for survival outcomes utilizing external controls for randomized clinical trials](https://mlsanity.com/paper/2410.18409/) (2024)
- [Statistical Inference for Heterogeneous Treatment Effect with Right-censored Data from Synthesizing Randomized Clinical Trials and Real-world Data](https://mlsanity.com/paper/2503.15745/) (2025)
- [Robust Estimation and Inference in Hybrid Controlled Trials for Binary Outcomes: A Case Study on Non-Small Cell Lung Cancer](https://mlsanity.com/paper/2505.00217/) (2025)
- [LEAP: The latent exchangeability prior for borrowing information from historical data](https://mlsanity.com/paper/2303.05223/) (2023)

## Abstract (arXiv)

Randomized controlled trials (RCTs)have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external validity. Studies using real-world data (RWD), historically considered less reliable for establishing causality, are now recognized as an important source of real-world evidence (RWE). In parallel, artificial intelligence and machine learning (AI/ML) are increasingly used throughout the drug development process, providing scalability and flexibility but also presenting challenges in interpretability and statistical rigor. This Perspective argues that the future of evidence generation will not depend on RCTs versus RWD, or statistics versus AI/ML, but on their principled integration under a statistical evidence framework that clarifies estimands, evaluates data fitness, controls bias, quantifies uncertainty, and determines when evidence is strong enough to support decisions. Building on the Causal Roadmap for high-quality real-world evidence, we present a six-step statistical roadmap for integrative evidence synthesis and organize the discussion around five core questions that statisticians must confront: when RWD is fit for causal use and when it is not; what AI genuinely contributes across the evidence lifecycle; what remains distinctly statistical and indispensable; how to build trustworthy end-to-end evidence systems in pharmaceutical, regulatory, and industry settings; and how statistical training should evolve.