From Correlation to Causation: Why Pharma Is Embracing Causal AI
- Esther Chen
- Sep 7
- 6 min read
Why causal AI is becoming pharma’s next strategic differentiator — from target validation to clinical trials and real-world evidence.
Introduction: The Limits of Correlation in Drug Discovery
Drug discovery today generates unprecedented volumes of data. Omics, imaging, electronic health records, and real-world evidence (RWE) are fueling machine learning models that find thousands of potential associations between targets, biomarkers, drugs, indications, and outcomes. Yet, correlation alone rarely delivers clinical success. It often fails to answer the critical question every scientist faces:
If we intervene on this target, pathway, or patient population, what will actually happen?
The industry’s hard truth: Many “promising” targets don’t alter disease biology when perturbed. Clinical trials fail when patient selection overlooks causal drivers. Observational studies mislead when confounding biases remain hidden.
This is where Causal AI comes in. Causal AI — a set of methods grounded in decades of statistical and scientific theory, now adapted into AI platforms that can reason not only about correlations, but also about interventions and counterfactuals [1, 2]. By modeling cause-and-effect relationships, causal AI provides a framework for moving from prediction to actionable decision-making, transforming how pharma approaches drug discovery and development.
Ladder of Causation: Framework for Thinking Beyond Data
Judea Pearl, a pioneer in causal inference and awarded the Turing Award in 2011, formalized a framework known as the Ladder of Causation. It explains the progression from descriptive to truly explanatory reasoning [1, 3]:
Association (seeing): “If we observe X, we can predict Y.” Example: Detecting correlations, e.g., “Smokers tend to develop lung cancer.”
Intervention (doing): “If we change X, what happens to Y?” Example: Asking what happens if we act, e.g., “If we reduce smoking, will risk of lung cancer decrease?”
Counterfactuals (imagining): “What would have happened to Y if X had been different?” Example: Exploring alternative realities, e.g., “Would this patient have survived if they had taken the drug?”
For pharma teams, the ladder captures a key message: correlation (seeing) is only the first rung. The real questions in drug discovery — Will inhibiting this protein reduce disease? What happens if we adjust dosing? Which patients would have responded if treated differently? — live on the higher rungs of intervention and counterfactuals.
The Double Helix of Causal Inference
Judea Pearl has described two fundamental laws of causal inference as “the double helix of causal thinking, entwining data and reality” in a 2025 talk at Genentech [5]. For pharma, this framework underscores why traditional machine learning — which stops at correlations — is not enough. To design interventions that succeed in the clinic, we must climb the higher rungs of the causal ladder.
The Law of Counterfactuals: What would have happened if something else had occurred?
The Law of Conditional Independence: How can we test if our assumptions about the world are reflected in the data?
Together, these principles form what Pearl calls the “double helix” of causal inference — a conceptual backbone as foundational to evidence generation as DNA is to biology.
Nobel Recognition: Causal Inference Goes Mainstream
The importance of causality isn’t limited to computer science. In 2021, the Nobel Prize in Economics was awarded to David Card, Joshua Angrist, and Guido Imbens for their pioneering work in natural experiments and causal inference methods [5].
The Nobel Committee emphasized that their methods revolutionized empirical research by showing how causal questions could be answered even outside randomized controlled trials (RCTs). While originally developed in labor economics, these approaches now influence medicine, epidemiology, and pharmaceutical R&D [6, 7].
For the life sciences, this Nobel recognition signaled a paradigm shift: causal methods are not fringe academic ideas — they are becoming the new standard of evidence across disciplines.
Why Causal AI Matters for Pharma R&D
Pharma companies increasingly face three recurring challenges:
Target validation
Many drug candidates look promising in high-throughput screens but fail in humans.
Causal inference methods such as Mendelian randomization (MR) treat genetic variation as a natural experiment, testing whether altering a biomarker causally changes disease risk [8-10].
Clinical trial design
Trials fail when inclusion criteria, endpoints, or comparator arms are misaligned with causal drivers of outcomes.
Causal models can refine patient stratification, predict heterogeneous treatment effects, and simulate “what if” scenarios to optimize trial design before execution [11, 12].
Real-world evidence (RWE)
Regulators and payers increasingly request evidence from real-world data, but observational studies are prone to confounding and bias.
Target trial emulation (TTE) applies causal reasoning to mimic randomized trials in RWE settings, producing more reliable insights for label expansion, safety, and HEOR [13-16].
Complementary Roles: Machine Learning + Causal AI
Machine learning (ML) and Causal AI are often portrayed as competing, but in practice they are complementary [11, 12]:
Machine learning generates hypotheses by identifying associations, clusters, or patterns in massive data.
Causal AI adds the capability to discover cause-and-effect relationships and test hypotheses by determining whether those causal interaction networks truly reflect interventions that change outcomes, leading to actionable insights.
In drug discovery, this synergy looks like:
Using machine learning to detect biomarker clusters from transcriptomics.
Applying causal inference to distinguish whether biomarkers are likely to be causal drivers of disease biology or merely downstream correlates, a critical step in biomedical research and drug discovery [17].
Real-World Impact: From Data to Decisions
By embracing causal AI, pharma teams can realize tangible benefits:
Improved target selection with stronger mechanistic evidence.
Smarter trials with better inclusion criteria and endpoints.
Trustful real-world insights that adjust for bias and confounding.
Credible RWE using target trial emulation to deliver robust post-marketing insights for regulators, payers, and prescribers.
These shifts reduce risk, shorten timelines, and ultimately improve the probability of success — outcomes every Pharma R&D leader prioritizes.
What This Means for Pharma Leaders
The adoption of causal AI is no longer optional — it is becoming a strategic differentiator:
Executives: Improve portfolio ROI by prioritizing targets with causal evidence.
Scientists: Strengthen translational hypotheses and design more informative studies.
Regulatory and HEOR teams: Build confidence in RWE submissions with causal frameworks.
In short: Pharma’s data deluge needs not just more prediction, but better explanation.
Conclusion & Call to Action
Drug discovery success depends on more than finding patterns in data — it requires understanding cause and effect. From Pearl’s ladder of causation to Nobel Prize–winning methods in natural experiments, and from Mendelian randomization to target trial emulation, causal AI provides the framework to make better, faster, and more confident decisions.
At Vizuro, we’ve built these principles into Apotek, our causal AI and generative AI platform designed specifically for drug discovery and development. Over the coming weeks, this blog series will explore more case studies and practical applications.
References
[1] Pearl J. Three Layer Causal Hierarchy. UCLA Technical Report. PDF Link
[2] Pearl J, Judea, and Dana Mackenzie. The Book of Why: The New Science of Cause and Effect. Basic Books, 2018.
[3] Pearl J., interview on causal reasoning and AI. Link
[4] Pearl J, The Science of Cause and Effect, with a glimpse at Personalized Decision Making, talk at Genetech on March of 2025. Recording
[5] Nobel Prize Committee. Scientific Background of the 2021 Prize in Economics. Link
[6] Airiti Library. Causality and the Nobel Prize. 2024. Link
[7] Oxera. Causality and Natural Experiments: The 2021 Nobel Prize in Economic Sciences. Link
[8] Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008. Link
[9] Burgess, S. & Thompson, S. Mendelian randomization with fine‐mapped genetic data: Choosing from large numbers of correlated instrumental variables. Genet Epidemiol. 2017. Link
[10] Davies, N. M., Holmes, M. V., & Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018. Link
[11] DIA Global Forum. Correlation vs. Causation: How Causal AI is Helping Determine Key Connections in Healthcare and Clinical Trials. 2024. Link
[12] Nature article. How causal artificial intelligence is revolutionizing the pharmaceutical industry. 2024. Link
[13] Hernán, M.A. & Robins, J.M. Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. Am J Epidemiol, 2016. Link
[14] Antoine A, Pérol D, Robain M, Delaloge S, Lasset C, Drouet Y. Target trial emulation to assess real-world efficacy in the Epidemiological Strategy and Medical Economics metastatic breast cancer cohort. J Natl Cancer Inst. 2023. Link
[15] BECARIS Publishing. Target trial emulation in HTA RWD submissions: methodological evolution or industry revolution? 2024. Link
[16] Janda GS, Wallach JD, Dhodapkar MM, Ramachandran R, Ross JS. Feasibility of Emulating Clinical Trials Supporting US FDA Supplemental Indication Approvals of Drugs and Biologics. JAMA Intern Med. 2023. Link
[17] Pavlović M., Al Hajj G.S., Kanduri C., Pensar J., Wood M., Sollid L.M., Greiff V., Sandve G.K. Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics. 2022. arXiv:2204.09291 [q-bio.QM] Link
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