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A brief introduction to causal inference

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If you have a question about this talk, please contact Ben Karniely.

What is a causal effect, and where can we find them? This talk will start from randomized controlled trials, why this (often) gives the quantity we want, and how the randomization aspect is important. This leads to a discussion about the difference between seeing versus doing, and the general problem of confounding, which can be seen in examples such as Simpson’s paradox. We will see that the problem of causal inference can be separated into two main questions – inferring the causal effect, and causal discovery. We look through some approaches for these two questions. We then briefly discuss how these ideas may help us think about predictive models.

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This talk is part of the Wednesday Seminars - Department of Computer Science and Technology series.

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