Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.
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Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.
In this paper we introduce RankPL, a modeling language that can be thought of as a qualitative variant of a probabilistic programming language with a semantics based on Spohn's ranking theory.
We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation.
SOTA for Causal Inference on IDHP
Causal inference analysis is the estimation of the effects of actions on outcomes.
Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets.
Notable advantages of our method over existing matching procedures are its high-quality matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible.
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias.
Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator.