이상학 연구실

How can we learn what causes what, and use that knowledge to make better decisions? At the Causality Lab, we connect mathematical questions about what can be learned with statistical methods and algorithms for learning it. We study when causal effects and counterfactuals can be uniquely determined from the available data and assumptions, how to bound them when they cannot, and how to estimate them from finite samples. This includes combining observations and experiments, transferring conclusions across settings, and understanding which variables and data support are needed for valid inference. We also develop models for estimating outcome distributions under interventions over time. We develop statistical tests and algorithms for learning causal structure, including settings with feedback loops and relationships among multiple entities. The challenges are both statistical and computational: determining which structures the data can distinguish, and searching large spaces of possible graphs. Our approaches include constraint-based learning, score-based search, and logical reasoning. For example, our deduction-based methods use relationships already established by simpler tests to avoid some harder tests. We use causal models to study how an agent should act and learn. This includes choosing which variables to control, which information a policy should use, and which interventions are worth exploring. Our work ranges from characterizing optimal policies to narrowing the action space when the causal structure is only partially known, and expanding it to include realizable counterfactual actions. We study how causal assumptions can guide representation learning, generative modeling, and generalization, and how they can help diagnose biases in learning systems. One line of w