Causal AI for Real-World Data

Causal Foundation Models

Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for every new problem: first proposing a causal mechanism, selecting a compatible estimator, and finally training it. Meanwhile, across diverse settings and modalities, much of machine learning has shifted to the paradigm of foundation models: networks pretrained once at scale and applied to new tasks without fine-tuning. Causal foundation models (CFMs) bring this paradigm to causal inference. CFMs are pretrained neural networks that estimate causal quantities, such as the average treatment effect, on entirely new datasets using in-context learning without requiring model updates. This work provides a practical introduction to this emerging area. We summarize the necessary background in causal inference and machine learning before discussing CFMs. Throughout, we include example code and Jupyter notebooks.

Causal Machine Learning for Discovering Actionable Insights in Observational Data

Traditional machine learning models achieve strong predictive performance but often are unable to reliably uncover causal relationships required for reliable decision-making, particularly in observational data where controlled experiments are not feasible. This limitation creates a critical gap between prediction and actionable insight, as correlation-based models are vulnerable to confounding bias and poor generalization under distributional shifts. To address this challenge, this study proposes a unified causal machine learning framework that integrates structural causal modeling with data-driven estimation techniques to enable robust causal discovery and effect estimation. The methodology combines hybrid causal structure learning (constraint-based and score-based approaches) with advanced causal effect estimation methods, including propensity score techniques and doubly robust estimators. The framework is evaluated on both synthetic datasets with known causal structures and real-world datasets to assess its accuracy, robustness, and interpretability. Experiments are conducted using multiple runs with controlled settings to ensure reproducibility and statistical validity. The results demonstrate that the proposed framework significantly outperforms traditional predictive models and standalone causal methods. It achieves higher causal discovery accuracy with improved precision and recall of causal edges, reduces estimation error in Average Treatment Effect (ATE), and maintains stable predictive performance under distributional shifts. Statistical analysis confirms significant improvements (p < 0.01) with large effect sizes, indicating strong reliability and robustness. This research aims to bridge the gap between prediction and explanation by enabling machine learning systems to generate actionable, interpretable, and causally valid insights. The findings highlight the importance of integrating causal reasoning into data science workflows to support informed decision-making, intervention planning, and trustworthy AI development.

Neural Causal Models Under Markov Equivalence

When and Why LLM Causal Priors Help: Closed-Loop Prior Selection for Amortized Causal Inference

Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundational task. Prior-data fitted networks (PFNs) amortize the task: a model trained on large numbers of programmatically generated synthetic causal tasks reads a new problem's observational data into context and returns an interventional-effect estimate in a single forward pass. The capability of such models is largely determined by the synthetic training prior, which is currently designed by hand, a bottleneck acknowledged by both Do-PFN and CausalPFN. Large language models (LLMs) can now ``draw''plausible causal graphs for a given domain, suggesting that LLM-distilled graphs could serve as prior material. Whether injecting such graphs helps at all, where any gain comes from, and when injection helps. Practice has so far relied on manual trial and error. We propose a \emph{closed-loop prior selection framework} that casts prior injection as a budget-constrained optimization over a candidate prior pool. Candidates undergo cheap post-training and are scored by a composite metric dominated by real-domain generalization; the winner then receives full training and paired statistical validation. On a 7.34M-parameter Do-PFN, the framework's winner attains a formally significant $2.75\times$ gain on the primary evaluation domain, and its error falls below that of the uninjected official base. Generalization on an adjacent monitoring domain improves significantly, and no monitored capability degrades. Mechanism experiments show that the gain depends on the semantic content of the distilled graph rather than its structural diversity alone does not produce it (directional evidence). With this framework and this regularity in hand, the use of LLM causal priors stops being manual trial and error and becomes an empirically verifiable selection problem.

Expert-Guided g-computation with Large Language Models for Estimating Causal Effects on Timings: Applications to Hospital Quality Improvement

Hospital quality improvement (QI) programs routinely face multiple candidate interventions to optimize hospital flow, but existing methods struggle to estimate and rank the causal effects of such interventions. This work focuses on one of the most standard hospital metrics, the average length of stay (LOS), and its causal estimand, the average time saved. To characterize this causal effect, qualitative approaches rely on expert judgment to map patient trajectories, making them susceptible to cognitive biases; quantitative approaches rely on data-driven models, which fail when interventions are hypothetical with no historical data or have complex causal mechanisms that require clinical reasoning rather than data alone. We propose expert-guided g-computation, or egg-computation, which combines the complementary strengths of both approaches by connecting the Gantt charts commonly used to map patient trajectories with the causal DAG literature. We introduce a causal model over Gantt charts and establish identification using a variant of g-computation that seeks expert input only for components unidentifiable from data. To make egg-computation practical, we develop an LLM-assisted pipeline that reliably scales up expert reasoning. In simulations, egg-computation outperforms conventional causal inference methods when patients have diverse causal structures and intervention mechanisms. In a study of eleven candidate QI interventions at an urban safety-net hospital, the LLM pipeline generated graphs and time-saving estimates highly concordant with those of human experts. Beyond healthcare, egg-computation is a broadly applicable framework for estimating the average time saved for candidate interventions whose causal mechanisms can be represented using Gantt charts.

Methods for causal inference from gene perturbation experiments and validation