Causal Inference - Groups

Bayesian nonparametric modeling for causal inference

Bayesian nonparametric modeling for causal inference.

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ACML 2021

The dataset used in this paper is a causal graph representing the bail decision process. It includes variables such as race, gender, age, literacy, employment, and case...

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Orthogonal Machine Learning: Power and Limitations

The dataset used in the paper is a synthetic demand estimation problem with dense dependence on observables.

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BD-HSIC dataset

The dataset used in this paper is a synthetic dataset for testing causal inference methods.

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Ponpare dataset

A public dataset with a coupons website, suitable for evaluating the proposed methodology.

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Adjusting for Nonignorable Drop-out Using Semiparametric Nonresponse Models

The paper discusses the doubly robust estimator for missing data and causal inference models.

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Doubly Robust Estimation in Missing Data and Causal Inference Models

The paper discusses the doubly robust estimator for missing data and causal inference models.

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Front-Door Adjustment Formula

The dataset used in the paper is a causal Bayesian network with a latent variable U.

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Long-term Causal Inference Under Persistent Confounding via Data Combination

The dataset used in the paper is a combination of experimental and observational data for long-term causal inference. It includes short-term outcomes and long-term outcomes, and...

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Dynamic Treatment Eﬀects with a Static Binary Instrumental Variable (IV)

The dataset used in the paper to analyze dynamic treatment eﬀects with a static binary instrumental variable (IV).

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Primary Biliary Cholangitis

The Primary Biliary Cholangitis dataset contains samples from 258 patients with PBC who entered into a randomized clinical trial.

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T ¨ubingen cause-effect pairs

The T ¨ubingen cause-effect pairs dataset contains 108 datasets of real cause-effect pairs.

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Bayesian causal inference via probabilistic program synthesis

The dataset used in the paper is a set of probabilistic programs that generate, edit, and interpret the source code of causal models.

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On the identifiability of the post-nonlinear causal model

The dataset used in the paper is a synthetic dataset generated with various structural equation types for all three forms of causal queries.

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Diffusion-based Causal Models

The dataset used in the paper is a synthetic dataset generated with various structural equation types for all three forms of causal queries.

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Invariant Representation Learning for Treatment Effect Estimation

The dataset used in the paper is a collection of multiple datasets from different environments, each containing treatment, outcome, and covariate information.