Causal machine learning: A survey and open problems
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …
that formalize the data-generation process as a structural causal model (SCM). This …
Transportability for bandits with data from different environments
A unifying theme in the design of intelligent agents is to efficiently optimize a policy based on
what prior knowledge of the problem is available and what actions can be taken to learn …
what prior knowledge of the problem is available and what actions can be taken to learn …
A note on efficient minimum cost adjustment sets in causal graphical models
E Smucler, A Rotnitzky - Journal of Causal Inference, 2022 - degruyter.com
We study the selection of adjustment sets for estimating the interventional mean under an
individualized treatment rule. We assume a non-parametric causal graphical model with …
individualized treatment rule. We assume a non-parametric causal graphical model with …
Catoni-style confidence sequences under infinite variance
In this paper, we provide an extension of confidence sequences for settings where the
variance of the data-generating distribution does not exist or is infinite. Confidence …
variance of the data-generating distribution does not exist or is infinite. Confidence …