If you're looking for real uncertainty, stop guessing with Bayes. Start guaranteeing with conformal prediction.
If you're looking for real uncertainty, stop guessing with Bayes. Start guaranteeing with conformal prediction.
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This is next-gen causal inference—ready for high-stakes, high-noise, high-dimensional environments.
As we've discussed in the post about Conditional Density Estimation, having a sense of confidence associated with your prediction is important for decision making [1], and this is no different in applications of causal inference. Here, estimating confidence intervals for the Conditional Average Treatment Effect (CATE) can greatly enhance the validity of causal inference studies.
Valeriy M., PhD, MBA, CQF