Causal Inference

Causal inference is the problem of determining the effect of interventions — not merely predicting what will happen under the existing distribution of conditions, but predicting what would happen if you changed something. The distinction between correlation and causation is not philosophical pedantry; it is the difference between a model that can inform action and one that cannot.

The foundational framework is the potential outcomes model (Rubin causal model): for each unit and each possible intervention, there is a potential outcome. The causal effect of an intervention is the difference between the potential outcome under that intervention and the potential outcome under no intervention. The fundamental problem of causal inference is that only one potential outcome is ever observed — you cannot simultaneously treat and not treat the same patient. Causal claims are therefore always about counterfactuals that cannot be directly observed.

Machine learning learns correlations from observational data. Correlations are not causal effects. A model trained on historical data will correctly predict that ice cream sales and drowning rates are correlated, without having any information about whether ice cream causes drowning (it does not — both correlate with summer). Deployed interventions based on correlational models can actively harm outcomes when the correlation was confounded. Most of the failures of data-driven decision-making in medicine, criminal justice, and social policy trace to this confusion.

The tools of causal inference — randomized controlled trials, instrumental variables, regression discontinuity, difference-in-differences — are designed to recover causal effects from data that cannot be assumed to be experimental. Each rests on assumptions that cannot be verified from the data alone; they must be defended on domain grounds. Judea Pearl's do-calculus provides a formal framework for reasoning about interventions given a causal graph. The field remains contested at its foundations, but the necessity of going beyond correlational statistics for decision-relevant claims is not.

The Causal Inference Problem in Machine Learning

Contemporary machine learning systems operate almost entirely in the correlational regime. They are trained to minimize prediction error over a training distribution, which means they learn whatever statistical regularities predict labels — causal or not. This is distributional shift expressed at the level of mechanism: a model trained on confounded correlations will fail not only when inputs shift, but when the confounding structure changes, because its predictions were tracking the confounder, not the cause.

The gap between correlation and causation in deployed AI systems has measurable consequences. The shortcut learning phenomenon — where neural networks exploit spurious correlations in training data rather than causally relevant features — produces models that are locally accurate and systematically wrong. A model that classifies medical images by correlating with artifact patterns rather than pathological features has justified true beliefs (in the training distribution) that are Gettier cases: they are correct by coincidence, not by genuine causal tracking.

The tools of causal inference — instrumental variables, regression discontinuity, do-calculus — are rarely applied in machine learning deployment because they require a specified causal graph, and machine learning systems do not produce causal graphs. They produce association tables. The integration of causal reasoning into AI systems — what Pearl calls 'the ladder of causation' (association, intervention, counterfactual) — remains an active research frontier with no working large-scale implementation. Until it is achieved, deploying machine learning systems for decisions that require causal knowledge — medical diagnosis, policy evaluation, safety-critical control — should be treated as epistemically irresponsible, not merely technically challenging.