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Bayesian nonparametric modeling for causal inference
Bayesian nonparametric modeling for causal inference. -
Orthogonal Machine Learning: Power and Limitations
The dataset used in the paper is a synthetic demand estimation problem with dense dependence on observables. -
BD-HSIC dataset
The dataset used in this paper is a synthetic dataset for testing causal inference methods. -
Ponpare dataset
A public dataset with a coupons website, suitable for evaluating the proposed methodology. -
Adjusting for Nonignorable Drop-out Using Semiparametric Nonresponse Models
The paper discusses the doubly robust estimator for missing data and causal inference models. -
Doubly Robust Estimation in Missing Data and Causal Inference Models
The paper discusses the doubly robust estimator for missing data and causal inference models. -
Front-Door Adjustment Formula
The dataset used in the paper is a causal Bayesian network with a latent variable U. -
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... -
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). -
Primary Biliary Cholangitis
The Primary Biliary Cholangitis dataset contains samples from 258 patients with PBC who entered into a randomized clinical trial. -
T ¨ubingen cause-effect pairs
The T ¨ubingen cause-effect pairs dataset contains 108 datasets of real cause-effect pairs. -
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. -
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. -
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. -
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. -
Monotone Function Estimation in the Presence of Extreme Data Coarsening
The dataset used in the paper for estimating the effect of maternal smoking on birth weight. -
Estimating Conditional Average Treatment Effects
The dataset used in the paper for estimating conditional average treatment effects. -
Towards Principled Causal Effect Estimation by Deep Identifiable Models
The dataset used in the paper for causal effect estimation using Intact-VAE. -
Shared Dynamics Reconstruction
The dataset used in the paper is a collection of time series data from diverse domains, including fluid dynamics, neuroscience, and systems biology.
