Hand extending seed to diverse group

Unlock Trial Insights: How to Broaden Research Impact Beyond the Lab

Lena Voss in Health & Wellness November 2025 • 5 min read.

"Bridging the Gap Between Clinical Trials and Real-World Populations for Better Health Outcomes"

Randomized Controlled Trials (RCTs) are essential for creating accurate estimates, serving as a key tool for causal inference research. These trials help researchers understand the real effects of interventions. However, limiting findings to the experimental group can restrict their usefulness. Achieving external validity, where results apply to broader populations, is crucial for wider scientific progress. Addressing these external validity challenges is the focus of current research and collaborative efforts.

A workshop at the Institute for Computational and Experimental Research in Mathematics (ICERM) in Fall 2023, gathered experts from different fields, including social science, medicine, public health, statistics, computer science, and education, to discuss obstacles in applying experimental results to diverse groups. This article consolidates ongoing efforts, highlights methodological overlaps across fields, reviews generalizability and transportability based on workshop discussions, and identifies remaining obstacles, suggesting paths for future research. By doing this, we aim to improve the understanding of how causal effects can be generalized and transported, encouraging collaboration across disciplines and providing valuable insights for those refining and applying causal inference methods.

This article will discuss three main topics from the workshop: (i) how to assess the external validity of trials, (ii) how to consider external validity beyond just the intention-to-treat effects, and (iii) how to use machine learning to improve external validity. We will place these advancements in the context of current research. After discussing these themes, the article will conclude by identifying five key areas for future research, including the problems and possibilities, and list all talk titles and speakers in the Appendix.

Validity of Identifying Assumptions Under Covariate Shifts

When aiming to make externally valid causal effect estimates, a primary source of bias arises from differences in the distribution of treatment effect moderators between the experimental sample and the target population. Treatment effect moderators are covariates that describe how the treatment will differentially affect individuals. As a result, researchers must adjust for the covariate shift in the underlying moderators between the experimental sample and the target population to estimate a valid causal effect across a target population of interest. Two common identifying assumptions leveraged in practice are (1) selection on observables (also referred to as conditional exchangeability) and (2) positivity of trial participation.

The first assumption, selection on observables, assumes that researchers can adjust for all distributional differences in moderators between the experimental sample and the target population. However, researchers frequently lack comprehensive covariate data for the target population, rely on inconsistent data sources like electronic health records or surveys, and may face measurement differences and the challenge of identifying all relevant moderators. The second assumption, the positivity of trial participation, requires that all units in the target population have a non-zero chance of being included in the experimental sample, but logistical, financial, and ethical constraints can violate this.

Understanding Treatment Effect Heterogeneity: Selection on observables and positivity of trial participation rely on researchers knowing which covariates moderate the treatment effect of the intervention of interest. This is important for both the design stage of an experiment, and for post-hoc adjustments.
Developing Methods for Researchers to Incorporate Substantive Knowledge: Incorporating substantive knowledge into research methodologies is crucial for formulating effective policy recommendations, especially when utilizing existing experimental data in new domains.
Assessing Validity of Identifying Assumptions: Sensitivity analyses allow researchers to consider the robustness of results to violations of the underlying assumptions.

While these assumptions help researchers theoretically identify the target population's average treatment effect, they can be difficult to justify in practice. Several recent advancements focus on methods to help researchers consider the validity of the underlying identifying assumptions. Let's summarize the main ideas.

Future Directions and Challenges

Our paper summarizes some of the recent advancements in causal methods for generalizability and transportability, as presented at the ICERM workshop. The workshop focused on three key areas of focus: (i) evaluating the robustness of foundational assumptions, (ii) extending the scope of causal analysis beyond traditional intention-to-treat effects, and (iii) incorporating machine learning to enhance the applicability of findings across various contexts. The discussion at the workshop also highlighted several challenges in bridging the research-to-practice gap as well as interesting future research directions. We provide a few examples of key discussion points from the workshop.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2402.17042,

Title: Towards Generalizing Inferences From Trials To Target Populations

Subject: stat.me cs.ai cs.lg econ.em

Authors: Melody Y Huang, Harsh Parikh

Published: 26-02-2024

Everything You Need To Know

What is the primary goal of research discussed regarding clinical trials?

The primary goal of this research is to improve the understanding of how causal effects from clinical trials can be generalized and transported to broader populations. This involves enhancing the generalizability and transportability of causal inferences to ensure that the findings from Randomized Controlled Trials (RCTs) can benefit diverse groups, leading to better health outcomes and wider scientific impact. This includes addressing challenges in external validity and encouraging collaboration across disciplines to refine and apply causal inference methods.

What are 'treatment effect moderators' and why are they important in assessing the external validity of clinical trials?

Treatment effect moderators are covariates that describe how the treatment will differentially affect individuals. They are critical for assessing external validity because differences in the distribution of these moderators between the experimental sample (from the clinical trial) and the target population can lead to bias in causal effect estimates. Researchers must adjust for covariate shifts in the underlying moderators to estimate a valid causal effect across a target population of interest. Understanding these moderators is also important for both the design stage of an experiment and for post-hoc adjustments to the data.

What are the two common identifying assumptions used to make externally valid causal effect estimates?

The two common identifying assumptions are (1) selection on observables (also referred to as conditional exchangeability) and (2) positivity of trial participation. 'Selection on observables' assumes that researchers can adjust for all distributional differences in treatment effect moderators between the experimental sample and the target population. 'Positivity of trial participation' requires that all units in the target population have a non-zero chance of being included in the experimental sample. However, real-world constraints often make these assumptions difficult to satisfy.

How can machine learning improve external validity in the context of clinical trials?

The article mentions the use of machine learning as a key area to enhance the applicability of findings across various contexts, but it does not go into specifics. Generally, machine learning can help improve external validity by providing advanced methods for handling complex data, identifying important treatment effect moderators, and making predictions about how treatment effects might vary across different populations. It can also help improve the process of identifying the underlying assumptions and their validity.

What were the key discussion areas addressed at the ICERM workshop, and what challenges were highlighted?

The ICERM workshop focused on three main areas: (i) evaluating the robustness of foundational assumptions, (ii) extending the scope of causal analysis beyond traditional intention-to-treat effects, and (iii) incorporating machine learning to enhance the applicability of findings across various contexts. The discussion also highlighted several challenges in bridging the research-to-practice gap, with the ultimate goal of improving the generalizability and transportability of causal inferences. Moreover, the workshop served as a forum to identify future research directions for refining and applying causal inference methods.