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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 651–700 of 1722 papers

TitleStatusHype
FAIR: A Causal Framework for Accurately Inferring Judgments Reversals—0
Generalized Random Forests using Fixed-Point TreesCode0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems—0
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal InferenceCode1
Identifiable causal inference with noisy treatment and no side informationCode0
Can predictive models be used for causal inference?—0
Mitigating Cold-start Forecasting using Cold Causal Demand Forecasting Model—0
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding—0
Inferring dynamic regulatory interaction graphs from time series data with perturbations—0
A Brief Review of Hypernetworks in Deep LearningCode0
Leveraging text data for causal inference using electronic health recordsCode0
Can Large Language Models Infer Causation from Correlation?Code1
Task-specific experimental design for treatment effect estimation—0
A Causal Framework for Decomposing Spurious Variations—0
Timing Process Interventions with Causal Inference and Reinforcement Learning—0
Finding Counterfactually Optimal Action Sequences in Continuous State SpacesCode0
Intervention Generalization: A View from Factor Graph Models—0
Contagion Effect Estimation Using Proximal Embeddings—0
Individual Causal Inference Using Panel Data With Multiple Outcomes—0
Behavioral Causal Inference—0
Point Cloud Completion Guided by Prior Knowledge via Causal Inference—0
Inferring Individual Direct Causal Effects Under Heterogeneous Peer InfluenceCode0
Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers—0
Neuroevolutionary representations for learning heterogeneous treatment effectsCode0
T2TD: Text-3D Generation Model based on Prior Knowledge Guidance—0
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects EstimationCode0
Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry—0
Covariate balancing using the integral probability metric for causal inferenceCode0
How Fragile is Relation Extraction under Entity Replacements?Code0
Causality-Aided Trade-off Analysis for Machine Learning Fairness—0
The Decaying Missing-at-Random Framework: Model Doubly Robust Causal Inference with Partially Labeled Data—0
uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative FilteringCode0
Estimation Beyond Data Reweighting: Kernel Method of MomentsCode0
Counterfactually Comparing Abstaining ClassifiersCode0
A Survey on Causal Discovery: Theory and Practice—0
The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study—0
The Hardness of Reasoning about Probabilities and Causality—0
A Causal Inference Framework for Leveraging External Controls in Hybrid Trials—0
Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward—0
Reinterpreting causal discovery as the task of predicting unobserved joint statistics—0
Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation—0
COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference PerspectiveCode1
Axiomatization of Interventional Probability Distributions—0
The Fundamental Limits of Structure-Agnostic Functional Estimation—0
Causal Discovery with Stage Variables for Health Time Series—0
String Diagrams with Factorized Densities—0
Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary PerspectiveCode0
CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing—0
Efficient estimation of weighted cumulative treatment effects by double/debiased machine learning—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