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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 601–650 of 1722 papers

TitleStatusHype
Imputation of Counterfactual Outcomes when the Errors are Predictable—0
CausalCellSegmenter: Causal Inference inspired Diversified Aggregation Convolution for Pathology Image Segmentation—0
Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media—0
Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation—0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door AdjustmentCode0
A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series—0
Out-of-distribution robustness for multivariate analysis via causal regularisation—0
The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing—0
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal InferenceCode0
History-dependence shapes causal inference of brain-behaviour relationshipsCode0
How to Understand "Support"? An Implicit-enhanced Causal Inference Approach for Weakly-supervised Phrase Grounding—0
Identifying Assumptions and Research Dynamics—0
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect EstimatorsCode0
Treatment effects without multicollinearity? Temporal order and the Gram-Schmidt process in causal inferenceCode0
Towards Generalizing Inferences from Trials to Target Populations—0
ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction ApplicationsCode0
Impact of Physical Activity on Quality of Life During Pregnancy: A Causal ML Approach—0
Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models—0
Statistical Agnostic Regression: a machine learning method to validate regression models—0
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation—0
Optimizing Language Models for Human Preferences is a Causal Inference Problem—0
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive OrdersCode0
Causal hybrid modeling with double machine learningCode0
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments—0
How causal inference concepts can guide research into the effects of climate on infectious diseases—0
Robust agents learn causal world models—0
Manipulation Test for Multidimensional RDD—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
Conditional Generative Models are Sufficient to Sample from Any Causal Effect EstimandCode0
Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk—0
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study—0
A flexible Bayesian g-formula for causal survival analyses with time-dependent confoundingCode0
PresAIse, A Prescriptive AI Solution for Enterprises—0
Integrating Large Language Models in Causal Discovery: A Statistical Causal ApproachCode0
Bayesian Causal Inference with Gaussian Process NetworksCode0
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data—0
Explaining Text Classifiers with Counterfactual RepresentationsCode0
How Being Inside or Outside of Buildings Affects the Causal Relationship Between Weather and Pain Among People Living with Chronic Pain—0
Causal Coordinated Concurrent Reinforcement Learning—0
Continuous Treatment Effects with Surrogate Outcomes—0
Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity—0
Position: AI/ML Influencers Have a Place in the Academic Process—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach—0
Deep Learning-based Group Causal Inference in Multivariate Time-seriesCode0
Modeling Latent Selection with Structural Causal Models—0
Proximal Causal Inference With Text DataCode0
Deep Learning With DAGs—0
Valid causal inference with unobserved confounding in high-dimensional settingsCode0
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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