SOTAVerified

Causal Inference

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 )

Papers

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Showing 1301–1350 of 1722 papers

TitleStatusHype
Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU—0
CaM-Gen:Causally-aware Metric-guided Text Generation—0
Counterfactual Representation Learning with Balancing Weights—0
Should Graph Convolution Trust Neighbors? A Simple Causal Inference MethodCode1
Poincare: Recommending Publication Venues via Treatment Effect EstimationCode0
Causal Discovery using Compression-Complexity MeasuresCode0
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges—0
Causal Inference in the Presence of Interference in Sponsored Search Advertising—0
Double Robust Representation Learning for Counterfactual PredictionCode0
Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeastCode0
Causal Multi-Level Fairness—0
A Graph Neural Network Framework for Causal Inference in Brain NetworksCode1
Counterfactual Variable Control for Robust and Interpretable Question AnsweringCode1
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification—0
Entropic Causal Inference for Neurological Applications—0
The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy—0
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference—0
Targeted VAE: Structured Inference and Targeted Learning for Causal Parameter Estimation—0
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal EffectCode1
Targeted VAE: Variational and Targeted Learning for Causal InferenceCode0
Causal Intervention for Weakly-Supervised Semantic Segmentation—0
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference—0
Latent State Inference in a Spatiotemporal Generative Model—0
Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment EffectsCode1
Chemical Property Prediction Under Experimental Biases—0
Counterfactual Explanation and Causal Inference in Service of Robustness in Robot Control—0
How-to Present News on Social Media: A Causal Analysis of Editing News Headlines for Boosting User EngagementCode0
Causal Inference of General Treatment Effects using Neural Networks with A Diverging Number of Confounders—0
Matching in Selective and Balanced Representation Space for Treatment Effects Estimation—0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
Semi-supervised learning and the question of true versus estimated propensity scoresCode0
Local Composite Quantile Regression for Regression Discontinuity—0
Quantifying the Causal Effects of Conversational Tendencies—0
Causal Inference in Possibly Nonlinear Factor ModelsCode0
Path Dependent Structural Equation Models—0
Hi-CI: Deep Causal Inference in High Dimensions—0
Long-Term Effect Estimation with Surrogate RepresentationCode1
Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes—0
Estimating heterogeneous survival treatment effect in observational data using machine learningCode0
Estimating Causal Effects with the Neural Autoregressive Density EstimatorCode0
Signal Processing on Directed Graphs—0
Structural Causal Models Are (Solvable by) Credal NetworksCode0
Determining the Relevance of Features for Deep Neural Networks—0
Naïve regression requires weaker assumptions than factor models to adjust for multiple cause confounding—0
Computational Causal Inference—0
Moment-Matching Graph-Networks for Causal InferenceCode0
Autoregressive flow-based causal discovery and inferenceCode1
Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism—0
When deep learning meets causal inference: a computational framework for drug repurposing from real-world dataCode1
Causal Inference using Gaussian Processes with Structured Latent Confounders—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAverage Treatment Effect Error0.96—Unverified
2Balancing Linear RegressionAverage Treatment Effect Error0.93—Unverified
3k-NNAverage Treatment Effect Error0.79—Unverified
4CEVAEAverage Treatment Effect Error0.46—Unverified
5Balancing Neural NetworkAverage Treatment Effect Error0.42—Unverified
6Causal ForestAverage Treatment Effect Error0.4—Unverified
7BCAUS DRAverage Treatment Effect Error0.29—Unverified
8TARNetAverage Treatment Effect Error0.28—Unverified
9Counterfactual Regression + WASSAverage Treatment Effect Error0.27—Unverified
10MTDL-KNNAverage Treatment Effect Error0.23—Unverified
#ModelMetricClaimedVerifiedStatus
1CFR WASSAverage Treatment Effect on the Treated Error0.09—Unverified
2CFR MMDAverage Treatment Effect on the Treated Error0.08—Unverified
3BARTAverage Treatment Effect on the Treated Error0.08—Unverified
4GANITEAverage Treatment Effect on the Treated Error0.06—Unverified
5BCAUSSAverage Treatment Effect on the Treated Error0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BARTAverage Treatment Effect Error0.34—Unverified
2OLS with separate regressors for each treatmentAverage Treatment Effect Error0.31—Unverified
3Average Treatment Effect Error-0.23—Unverified