50 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 )

CausalML is a Python implementation of algorithms related to causal inference and machine learning.

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).

Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e. g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach".

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.

Thus, the toolkit is agnostic to the machine learning model that is used.