Causal Inference Isn't Special: Why It's Just Another Prediction Problem
This perspective aims to demystify causal inference and make it more accessible to practitioners and educators by linking it to existing predictive modeling techniques.
The paper argues that causal inference is not fundamentally different from predictive modeling but rather a structured instance of prediction under distribution shift, where potential outcomes are selectively observed based on treatment assignment, and reframes it to connect with familiar tools like reweighting and domain adaptation.
Causal inference is often portrayed as fundamentally distinct from predictive modeling, with its own terminology, goals, and intellectual challenges. But at its core, causal inference is simply a structured instance of prediction under distribution shift. In both cases, we begin with labeled data from a source domain and seek to generalize to a target domain where outcomes are not observed. The key difference is that in causal inference, the labels -- potential outcomes -- are selectively observed based on treatment assignment, introducing bias that must be addressed through assumptions. This perspective reframes causal estimation as a familiar generalization problem and highlights how techniques from predictive modeling, such as reweighting and domain adaptation, apply directly to causal tasks. It also clarifies that causal assumptions are not uniquely strong -- they are simply more explicit. By viewing causal inference through the lens of prediction, we demystify its logic, connect it to familiar tools, and make it more accessible to practitioners and educators alike.