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Causality and Machine Learning

At Microsoft Research, our causality research spans a broad array of topics, including: using causal insights to improve machine learning methods; adapting and scaling causal methods to leverage large-scale and high-dimensional datasets; and applying all these methods for data-driven decision making in real-world contexts.

See our publications for examples of our work.

Machine Learning and Causal Reasoning: There is fertile interplay between machine learning and causal reasoning. Not only does machine learning provide the methods for conventional causal inference techniques to scale to leverage today’s large-scale, high-dimensional datasets for key policy-evaluation and quality decision-making, but computing approaches such as search algorithms are critical to creating AutoCausal – an automated data scientist that can integrate domain knowledge, validate causal assumptions, and tune hyper-parameters a la AutoML.  At the same time, causal reasoning methods and insights are directly related to core machine learning challenges of robustness, generalizability, bias, and explainability.   Moreover, understanding causality is widely seen as a key deficiency of current AI methods, and a necessary precursor for building more human-like machine intelligence.

Answering Causal Questions from Data: Identifying causal effects is an integral part of scientific inquiry, spanning a wide range of questions such as understanding behavior in online systems, effect of social policies, or risk factors for diseases.  As computing increasingly impact all walks of life, questions of cause-and-effect are also critical for the design and data-driven evaluation of all the computer systems and applications we build.  For instance, how do algorithmic recommendations affect our purchasing decisions?  How do they affect a student’s learning outcome or a doctor’s efficacy? These are hard questions and require thinking about the counterfactual: what would have happened in a world with a different system, policy, or intervention?  Without randomized experiments and causal reasoning, correlation-based methods can lead us astray.

Causal Tooling, Libraries, and Education: Complementing our core research and with the goal of broadening the use of causal methods across academia and industry, we strive to make our technologies accessible through open source tooling and libraries, such as DoWhy and EconML, and frequently present tutorials on new methods.