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Large Language Models as Co-Pilots for Causal Inference in Medical Studies

Ahmed Alaa, Rachael V. Phillips, Emre Kıcıman, Laura B. Balzer, Mark van der Laan, Maya Petersen

TL;DR

Real-world data studies enable causal insights beyond randomized trials but are prone to biases that can mislead decision-making. The paper proposes a causal co-pilot framework where large language models encode domain knowledge and interact with researchers to clarify questions, critique designs, and interpret results within established causal and regulatory frameworks. It details grounding in the Causal Roadmap and Target Trial Emulation, a Github Copilot–like architecture, and directions for grounding, finetuning, alignment, human factors, and evaluation. The authors argue that, with rigorous grounding and validation, LLM-powered co-pilots can reduce interdisciplinary burdens and enhance the reliability and transparency of real-world evidence in regulatory contexts, while acknowledging significant challenges and risks to address.

Abstract

The validity of medical studies based on real-world clinical data, such as observational studies, depends on critical assumptions necessary for drawing causal conclusions about medical interventions. Many published studies are flawed because they violate these assumptions and entail biases such as residual confounding, selection bias, and misalignment between treatment and measurement times. Although researchers are aware of these pitfalls, they continue to occur because anticipating and addressing them in the context of a specific study can be challenging without a large, often unwieldy, interdisciplinary team with extensive expertise. To address this expertise gap, we explore the use of large language models (LLMs) as co-pilot tools to assist researchers in identifying study design flaws that undermine the validity of causal inferences. We propose a conceptual framework for LLMs as causal co-pilots that encode domain knowledge across various fields, engaging with researchers in natural language interactions to provide contextualized assistance in study design. We provide illustrative examples of how LLMs can function as causal co-pilots, propose a structured framework for their grounding in existing causal inference frameworks, and highlight the unique challenges and opportunities in adapting LLMs for reliable use in epidemiological research.

Large Language Models as Co-Pilots for Causal Inference in Medical Studies

TL;DR

Real-world data studies enable causal insights beyond randomized trials but are prone to biases that can mislead decision-making. The paper proposes a causal co-pilot framework where large language models encode domain knowledge and interact with researchers to clarify questions, critique designs, and interpret results within established causal and regulatory frameworks. It details grounding in the Causal Roadmap and Target Trial Emulation, a Github Copilot–like architecture, and directions for grounding, finetuning, alignment, human factors, and evaluation. The authors argue that, with rigorous grounding and validation, LLM-powered co-pilots can reduce interdisciplinary burdens and enhance the reliability and transparency of real-world evidence in regulatory contexts, while acknowledging significant challenges and risks to address.

Abstract

The validity of medical studies based on real-world clinical data, such as observational studies, depends on critical assumptions necessary for drawing causal conclusions about medical interventions. Many published studies are flawed because they violate these assumptions and entail biases such as residual confounding, selection bias, and misalignment between treatment and measurement times. Although researchers are aware of these pitfalls, they continue to occur because anticipating and addressing them in the context of a specific study can be challenging without a large, often unwieldy, interdisciplinary team with extensive expertise. To address this expertise gap, we explore the use of large language models (LLMs) as co-pilot tools to assist researchers in identifying study design flaws that undermine the validity of causal inferences. We propose a conceptual framework for LLMs as causal co-pilots that encode domain knowledge across various fields, engaging with researchers in natural language interactions to provide contextualized assistance in study design. We provide illustrative examples of how LLMs can function as causal co-pilots, propose a structured framework for their grounding in existing causal inference frameworks, and highlight the unique challenges and opportunities in adapting LLMs for reliable use in epidemiological research.
Paper Structure (20 sections, 3 figures)

This paper contains 20 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Landscape of Medical Studies
  4. Frameworks for RWE Generation
  5. How can LLMs Help Avoid Flaws in RWD Studies?
  6. Clarifying the Causal Question
  7. Critiquing Study Design
  8. Interpreting Study Results
  9. The Causal Co-Pilot Framework
  10. Role of LLM Co-Pilots in RWD Studies
  11. Causal Co-Pilot System Architecture
  12. Developing a Causal Co-Pilot: Research Directions & Challenges
  13. Grounding and Structured Decoding: Regulatory and Causal Frameworks
  14. Finetuning: Instruction-Tuning Data for the Causal Co-Pilot
  15. Alignment: Learning from Peer Review
  16. ...and 5 more sections

Figures (3)

  • Figure 1: Overview of the Medical Causal Co-Pilot Framework: The causal co-pilot engages with input prompts from users (e.g., clinical and biopharmaceutical researchers) related to a causal question with clinical equipoise, such as determining if a drug causes an increase in the incidence of a future adverse event. Users provide contextual information on the RWD being utilized. The causal co-pilot then refines prompt specificity by grounding it in clinical and statistical domain knowledge, as well as regulatory guidance and analytic frameworks for causal inference. This collaborative interaction results in a rigorous and transparent study aimed at addressing the causal question using the RWD at hand.
  • Figure 2: Landscape of medical studies and RWE frameworks. Panel A is adapted from concato2020randomized and panel C is adapted from dang2023causalpetersen2014causal.
  • Figure 3: Demonstrating the Capabilities of LLMs as Causal Co-pilots: We assess the ability of GPT-4 to analyze the design and results of the observational studies in Sections \ref{['Sec21']}, \ref{['Sec22']}, and \ref{['Sec23']} through various forms and modalities of interaction with a human user. Panel A: The user directly queries the LLM co-pilot about the appropriate causal question for designing a study on the impact of postmenopausal hormone therapy on cardiovascular risk. Panel B: The user supplies the entire text of the published study in graaf2004risk and asks the LLM co-pilot to evaluate if the design is prone to immortal time bias. Panels C & D: The user requests the LLM to assess the validity of study results based on visual inputs in the form of cumulative hazard curves. (Visual inputs in Panels C & D are derived from Figure 1 & 2 in aggarwal2023real and hammond2022oral, respectively. Copyright clearances and permissions to reuse both figures were obtained from the original publishers.)