# Causal Inference

463 papers with code • 3 benchmarks • 8 datasets

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 )

## Libraries

Use these libraries to find Causal Inference models and implementations## Most implemented papers

# Causal Effect Inference with Deep Latent-Variable Models

Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers.

# Unbiased Scene Graph Generation from Biased Training

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".

# Adapting Neural Networks for the Estimation of Treatment Effects

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

# Structural Intervention Distance (SID) for Evaluating Causal Graphs

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

# Estimating individual treatment effect: generalization bounds and algorithms

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.

# Double/Debiased Machine Learning for Treatment and Causal Parameters

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

# Adapting Text Embeddings for Causal Inference

To address this challenge, we develop causally sufficient embeddings, low-dimensional document representations that preserve sufficient information for causal identification and allow for efficient estimation of causal effects.

# DoWhy: An End-to-End Library for Causal Inference

In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent.

# BART: Bayesian additive regression trees

We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior.

# Counterfactual Fairness

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing.