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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 301–350 of 1722 papers

TitleStatusHype
A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal DataCode0
Do LLMs Have the Generalization Ability in Conducting Causal Inference?Code0
Do-calculus enables estimation of causal effects in partially observed biomolecular pathwaysCode0
Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time SeriesCode0
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional DistributionsCode0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
Diffusion Model in Causal Inference with Unmeasured ConfoundersCode0
Discovering Ancestral Instrumental Variables for Causal Inference from Observational DataCode0
Detecting clinician implicit biases in diagnoses using proximal causal inferenceCode0
Causal Falsification of Digital TwinsCode0
Detecting hidden confounding in observational data using multiple environmentsCode0
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved ConfoundersCode0
Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic ControlCode0
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep LearningCode0
A Primer on Deep Learning for Causal InferenceCode0
Deep representation learning for individualized treatment effect estimation using electronic health recordsCode0
Deep Counterfactual Networks with Propensity-DropoutCode0
Deep Causal Inference for Point-referenced Spatial Data with Continuous TreatmentsCode0
Deep Learning-based Group Causal Inference in Multivariate Time-seriesCode0
Deriving Causal Order from Single-Variable Interventions: Guarantees & AlgorithmCode0
Causal Effect Estimation using Variational Information BottleneckCode0
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized ExperimentsCode0
Debiased Bayesian inference for average treatment effectsCode0
Causal Effect Estimation on Hierarchical Spatial Graph DataCode0
Adversarial Generalized Method of MomentsCode0
Debiasing Recommendation by Learning Identifiable Latent ConfoundersCode0
Generalized Random Forests using Fixed-Point TreesCode0
DecoR: Deconfounding Time Series with Robust RegressionCode0
DAG-aware Transformer for Causal Effect EstimationCode0
Causal Discovery using Compression-Complexity MeasuresCode0
A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patientsCode0
Argumentative Causal DiscoveryCode0
Double Cross-fit Doubly Robust Estimators: Beyond Series RegressionCode0
Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation dataCode0
Counterfactual Prediction Under Selective ConfoundingCode0
Causal fault localisation in dataflow systemsCode0
Causal Feature Learning in the Social SciencesCode0
Detecting and Measuring Confounding Using Causal Mechanism ShiftsCode0
A Fast Bootstrap Algorithm for Causal Inference with Large DataCode0
A Practical Approach to Causal Inference over TimeCode0
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal InferenceCode0
Dirac Delta Regression: Conditional Density Estimation with Clinical TrialsCode0
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based TrainingCode0
Causal Discovery in Linear Structural Causal Models with Deterministic RelationsCode0
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender SystemsCode0
Approaching an unknown communication system by latent space exploration and causal inferenceCode0
Counterfactually Comparing Abstaining ClassifiersCode0
Causal discovery for observational sciences using supervised machine learningCode0
Adversarial Balancing for Causal InferenceCode0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
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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