39 papers with code ·
Miscellaneous

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )

We show that under mild assumptions on the consistency rate of the nuisance estimator, we can achieve the same error rate as an oracle with a priori knowledge of these nuisance parameters.

Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.

To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID).

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

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.

To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph.

A key insight is that causal adjustment requires only the aspects of text that are predictive of both the treatment and outcome.

Causal inference from observational data often assumes "ignorability," that all confounders are observed.

Causal inference analysis is the estimation of the effects of actions on outcomes.

We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects.