28 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.

We show that under mild assumption 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.

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.

Diviyan-Kalainathan/CausalDiscoveryToolbox •

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

ICML 2017 • clinicalml/cfrnet •

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

IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework

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

blei-lab/causal-text-embeddings •

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

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

EMNLP 2018 • zachwooddoughty/emnlp2018-causal

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.

Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions.