# Causal inference in drug discovery and development

arXiv: https://arxiv.org/abs/2209.14664 · HTML: https://mlsanity.com/paper/2209.14664/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 165 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [DoWhy: An End-to-End Library for Causal Inference](https://mlsanity.com/paper/2011.04216/) (2020)
- [Permutation-based Causal Inference Algorithms with Interventions](https://mlsanity.com/paper/1705.10220/) (2017)
- [External Validity: From Do-Calculus to Transportability Across Populations](https://mlsanity.com/paper/1503.01603/) (2015)
- [Granger Causality: A Review and Recent Advances](https://mlsanity.com/paper/2105.02675/) (2021)
- [Large-Scale Differentiable Causal Discovery of Factor Graphs](https://mlsanity.com/paper/2206.07824/) (2022)
- [Causal models for dynamical systems](https://mlsanity.com/paper/2001.06208/) (2020)
- [Simple rules for complex decisions](https://mlsanity.com/paper/1702.04690/) (2017)
- [Multiple Quantitative Trait Analysis Using Bayesian Networks](https://mlsanity.com/paper/1402.2905/) (2014)
- [Causal Inference in medicine and in health policy, a summary](https://mlsanity.com/paper/2105.04655/) (2021)
- [Estimating the treatment effect in a subgroup defined by an early post-baseline biomarker measurement in randomized clinical trials with time-to-event endpoint](https://mlsanity.com/paper/1806.08807/) (2018)
- [Weighted Approach for Estimating Effects in Principal Strata with Missing Data for a Categorical Post-Baseline Variable in Randomized Controlled Trials](https://mlsanity.com/paper/2101.04263/) (2021)

## Built on by (most cited first, 6 of 6)

- [Causal Effect Identification in LiNGAM Models with Latent Confounders](https://mlsanity.com/paper/2406.02049/) (2024)
- [Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants](https://mlsanity.com/paper/2506.05202/) (2025)
- [The Minimal Search Space for Conditional Causal Bandits](https://mlsanity.com/paper/2502.06577/) (2025)
- [ExDBN: Learning Dynamic Bayesian Networks using Extended Mixed-Integer Programming Formulations](https://mlsanity.com/paper/2410.16100/) (2024)
- [Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery](https://mlsanity.com/paper/2507.19568/) (2025)
- [Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data](https://mlsanity.com/paper/2602.23602/) (2026)

## Abstract (arXiv)

To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision making in drug discovery. While it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a non-technical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.