Home Research Papers Datasets State of the Art Pricing

Discover, visualize, and connect AI research papers. Explore the latest trends and insights in artificial intelligence research.

Product

Home
Research Papers
About

Support

Contact
Terms of Service
Privacy Policy

© 2026 Papersgraph. All rights reserved.

knowledge-base-9

Causal Inference

3260 papers • 126 benchmarks • 313 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 )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in causal-inference-19

Trend

Dataset

Best Model

Actions

IHDP

IHDP

Jobs

Jobs

IDHP

IDHP

Libraries

i

Use these libraries to find causal-inference-19 models and implementations

cjerzak/causalimages-software

3 papers 16

Datasets

IHDP

Jobs

ParaBank

ReCO

ChatGPT Advice Responses

ChatGPT Advice Responses

NTPairs

Subtasks

Counterfactual Inference

Most implemented papers

Unbiased Scene Graph Generation From Biased Training

Yulei Niu, Kaihua Tang, Jianqiang Huang, Jiaxin Shi, Hanwang Zhang•Wed Feb 26 2020

A novel SGG framework based on causal inference but not the conventional likelihood is presented, which uses Total Direct Effect (TDE) as the proposed final predicate score for unbiased SGG and can be widely applied in the community who seeks unbiased predictions.

787

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

microsoft/dowhy

2 papers 6,915

Microsoft/EconML

2 papers 3,674

2 papers 693

microsoft/causica

2 papers 419

ICLR Database

ICLR Database

NAIST COVID

0

Causal Effect Inference with Deep Latent-Variable Models

R. Zemel, Christos Louizos, Uri Shalit, D. Sontag, J. Mooij, Max Welling•Tue May 23 2017

This work builds on recent advances in latent variable modeling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect and shows its method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects.

838 0

Adapting Neural Networks for the Estimation of Treatment Effects

Victor Veitch, D. Blei, Claudia Shi•Tue Jun 04 2019

A new architecture is proposed, the Dragonnet, that exploits the sufficiency of the propensity score for estimation adjustment, and a regularization procedure is proposed that induces a bias towards models that have non-parametrically optimal asymptotic properties `out-of-the-box`.

453 0

Structural Intervention Distance (SID) for Evaluating Causal Graphs

J. Peters, Peter Buhlmann•Tue Jun 04 2013

The SID is based on a graphical criterion only and quantifies the closeness between two DAGs in terms of their corresponding causal inference statements, well-suited for evaluating graphs that are used for computing interventions.

44 0

Estimating individual treatment effect: generalization bounds and algorithms

Fredrik D. Johansson, Uri Shalit, D. Sontag•Sun Jun 12 2016

A novel, simple and intuitive generalization-error bound is given showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalized-error of that representation and the distance between the treated and control distributions induced by the representation.

1163 0

Double/Debiased Machine Learning for Treatment and Causal Parameters

Mert Demirer, Christian Hansen, V. Chernozhukov, D. Chetverikov, E. Duflo, W. Newey, J. Robins•Fri Jul 29 2016

This work can form an orthogonal score for the target low-dimensional parameter by combining auxiliary and main ML predictions, and build a de-biased estimator of the target parameter which typically will converge at the fastest possible 1/root(n) rate and be approximately unbiased and normal, and from which valid confidence intervals for these parameters of interest may be constructed.

153 0

Adapting Text Embeddings for Causal Inference

Victor Veitch, Dhanya Sridhar, D. Blei•Tue May 28 2019

A method to estimate causal effects from observational text data, adjusting for confounding features of the text such as the subject or writing quality, and studies causally sufficient embeddings with semi-synthetic datasets and finds that they improve causal estimation over related embedding methods.

119 0

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

Amit Sharma, Emre Kıcıman•Sun Nov 08 2020

DoWhy is an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions and supports interoperability with other implementations, such as EconML and CausalML for the estimation step.

240 0

BART: Bayesian Additive Regression Trees

H. Chipman, E. George, R. McCulloch•Wed Jun 18 2008

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.

1964 0

Counterfactual Fairness

Matt J. Kusner, Ricardo Silva, Chris Russell, Joshua R. Loftus•Sun Mar 19 2017

This paper develops a framework for modeling fairness using tools from causal inference and demonstrates the framework on a real-world problem of fair prediction of success in law school.

1785 0

Adding a benchmark result helps the community track progress.

Causal Inference | State-of-the-Art