Given high-dimensional data describing differences in characteristics between individuals, what state-of-the-art ML technique is best suited for estimating differences in causal effects?
I've recently updated my site to to allow readers to more easily access to my blog posts on introductory causal inference.
Oftentimes, analysts are interested how a particular intervention differentially affects an observed population. How can we estimate the extent of…
What are some of the challenges an analyst must be wary of when using propensity score matching for causal inference tasks?
How do we "control" for a large number of confounding variables when analyzing causal effects in an observational setting, and have very little control…
Why is causal inference so hard? What "adjustments" can we make to observational data in order to make it easier?
How can we estimate average treatment effects and what biases must we be wary of when evaluating our estimation?
Is there a way we describe the extent of causal relationships, in order to more wholly characterize quantifiable effects?
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Causal Flows