Kernel measures similarity of nonlinear causal structures in heterogeneous populations.
problem Learning causal structure in populations with diverse underlying structures.
method Distance covariance-based kernel for measuring similarity of causal structures.
result Kernel enables clustering of homogeneous subpopulations for causal structure learning.
This paper addresses external validity bias in causal inference.
problem Estimating causal effects in a target population.
method Synthesis of approaches for generalizability and transportability, including tests for heterogeneity of treatment effects and differences between study and target populations.
result Framework for addressing external validity bias in causal inference.
Paper tackles causal effect identification in sub-population with latent variables.
problem Identify causal effects in a sub-population with latent variables.
method Extend relevant graphical definitions and propose an algorithm for the s-ID problem.
result Sound algorithm for s-ID problem with latent variables.
Paper tackles estimating individual treatment effects from observational data.
problem Estimating the difference between outcomes with and without treatment from single observation.
method Formulated as inference from hidden variables, uses a model of four causal populations, proposes ECM algorithm.
result ECM algorithm provides better performance compared to baseline methods on synthetic and real-world data.
Estimates causal effect of managed care plans on NYC Medicaid spending.
problem Generalizing causal estimates to a target population not well-represented by randomized studies.
method Conditional cross-design synthesis estimators combining randomized and observational data.
result Estimates causal effect of managed care plans on health care spending.
Bayesian method identifies causal sets across populations without graph knowledge.
problem Transporting causal information across populations without causal graph knowledge.
method Combines observational and experimental data to identify s-admissible backdoor sets.
result Proves asymptotic convergence and corrects transportability bias in simulations.
Causal processes in biomedicine may contain cycles, evolve over time or differ between populations. However, many graphical models cannot accommodate these conditions. We propose to model causation using a mixture of directed cyclic graphs (DAGs), where the joint distribution in a population follows a DAG at any single…
Study causal inference under specific sampling methods with monotonicity assumptions.
problem Causal inference under biased sampling methods.
method Binary-outcome and binary-treatment case study with monotonicity assumptions.
result Monotonicity assumptions yield comparable results to random sampling.
Generalizing empirical findings to new environments, settings, or populations is essential in most scientific explorations. This article treats a particular problem of generalizability, called "transportability", defined as a license to transfer information learned in experimental studies to a different population, on …
New method resolves causal heterogeneity by defining a resolution profile.
problem Causal subgroup analyses often oversimplify heterogeneity into a small number of groups.
method Introduces a resolution profile as a functional of the causal feature law, using Bayesian-bootstrap inference.
result Shows that the resolution profile is a continuous path with discontinuities at knots, providing integer-valued subgroup numbers.
New approach to off-policy evaluation connects causal graph to policy effects.
problem Evaluating policies using observational data from different policies.
method Formalizes off-policy evaluation within a causal graph framework.
result Identifies specific causal estimands and highlights necessary experimental data.
New methods improve causal inference generalization using trial and observational data.
problem Limited trial data makes generalizing causal inferences to target populations statistically infeasible.
method Develops algorithms that combine trial and observational data to estimate complex nuisance functions.
result Improves generalization of causal inferences when the additional observational study is high-quality.
The study uses transfer learning to compare surgical outcomes across racial/ethnic subgroups.
problem Difficulty in comparing surgical outcomes due to racial/ethnic and geographic differences.
method Causal inference framework and transfer learning to incorporate data from multiple populations.
result Racial and ethnic differences in surgical outcomes are found, with non-Hispanic Black patients experiencing wide variability.
New method stops experiments early for harm in diverse groups.
problem Early stopping of experiments for harmful treatment effects in diverse populations.
method Causal machine learning approach (CLASH) for early stopping.
result CLASH effectively stops experiments early for harmful treatment effects in diverse groups.
Novel framework identifies pump-specific deterioration rates using Bayesian hierarchical hazard modeling and causal discovery.
problem Challenges in asset management due to heterogeneous deterioration rates in pump equipment.
method Bayesian hierarchical hazard modeling with causal discovery, GPU-accelerated No-U-Turn Sampling (NUTS), and DirectLiNGAM.
result Identified striking heterogeneity in deterioration rates, with negative effects 400 times larger than positive effects.
Study causal effects on humans in mixed human-AI systems with unobserved unit types.
problem Estimating causal effects on humans in systems with unobserved unit types and interaction networks.
method Assumed human-AI prior, causal message passing (CMP) framework, subpopulation analysis.
result Consistently recover human-specific causal effects using subpopulations with varying expected human composition and treatment exposure.
Study creates a multimodal learning framework for CVD risk prediction.
problem Predicting cardiovascular disease risk in diverse populations.
method Combines cross modal transformers, graph neural networks, and causal representation learning.
result Model predicts personalized CVD risk with causal invariance across subpopulations.
Bayesian meta-learning improves health prediction models across similar diseases.
problem Inter- and intra-task variability in healthcare predictions due to disease heterogeneity and patient differences.
method Bayesian meta-learning approach that models task similarity to mitigate negative transfer and improve generalizability.
result Significant generalizability improvements in stroke prediction tasks using electronic health record data.
Examines parallels between human subjects and texts for causal inference.
problem Ambiguity and fallacies in causal inference using textual data.
method Two strategies: shifting from traits to perceptions and from concepts to parts.
result Highlights the importance of clarifying fundamental concepts.
Unified framework for estimating indirect effects in observational studies with unmeasured confounding.
problem Challenges in evaluating indirect effects due to unmeasured confounding and unethical exposures.
method Developed a unified identification and estimation framework using proximal causal inference.
result Unified identification and estimation of PIIE and causal effect of an intervening variable in settings with pervasive unmeasured confounding.
Bayesian networks learn sub-population differences from data.
problem Inference from a single network structure can be misleading when data populations are heterogeneous.
method A mixture of Bayesian networks where component probabilities depend on individual characteristics.
result Identifies both network structures and demographic predictors of sub-population membership.
Proposes Causal k-Means Clustering to identify subgroup effects.
problem Identifying subgroup effects with heterogeneous treatment effects.
method Leverages k-means clustering to uncover unknown subgroup structure.
result Developed bias-corrected estimator with fast root-n rates and asymptotic normality.
Performing inference on data obtained through observational studies is becoming extremely relevant due to the widespread availability of data in fields such as healthcare, education, retail, etc. Furthermore, this data is accrued from multiple homogeneous subgroups of a heterogeneous population, and hence, generalizing…
CMDE uses deep learning to estimate causal effects from complex data.
problem Handling complex data structures like images for causal effect estimation.
method Causal Multi-task Deep Ensemble (CMDE) framework.
result CMDE outperforms state-of-the-art methods across various datasets and tasks.
Proposes a generalized causal tree for handling multiple treatments in uplift modeling.
problem Handling multiple treatments in uplift modeling.
method Generalizes causal tree algorithm to handle multiple discrete and continuous-valued treatments.
result Demonstrates improved performance over existing methods in experiments and real data examples.
Proposes a new method to handle data heterogeneity in causal inference.
problem Challenges of collaborating between different data centers due to heterogeneity.
method Collaborative inverse propensity score weighting estimator to adjust for distribution shift.
result Significant improvements over traditional meta-analysis methods when dealing with increased heterogeneity.
EP-learning framework improves causal contrast estimation efficiency.
problem Estimating heterogeneous causal contrasts efficiently and stably.
method EP-learning framework combining T-learning and DR-learning.
result EP-learners are oracle-efficient and outperform competitors.
CRE discovers interpretable subgroups with heterogeneous treatment effects.
problem Identifying subgroups with notable treatment effect heterogeneity.
method Causal Rule Ensemble (CRE) using an ensemble-of-trees approach.
result CRE offers interpretable decision rules and high stability in subgroup discovery.
Observational cohort studies with oversampled exposed subjects are typically implemented to understand the causal effect of a rare exposure. Because the distribution of exposed subjects in the sample differs from the source population, estimation of a propensity score function (i.e., probability of exposure given basel…
DiD-BCF model improves causal inference in panel data with robust non-parametric methods.
problem Challenges in Difference-in-Differences (DiD) estimation, especially heterogeneous treatment effects and non-linearities.
method Difference-in-Differences Bayesian Causal Forest (DiD-BCF) with PTA-based reparameterization.
result DiD-BCF provides superior performance and uncovers significant heterogeneity in treatment effects.
A-ICP selects experiments to learn causal effects efficiently.
problem Learning causal effects from observational data is difficult.
method Active learning framework based on Invariant Causal Prediction.
result Proposes intervention selection policies to reveal direct causes.
Develops a weighting framework to generalize ITRs from source to target populations.
problem Challenges in generalizing ITRs from a source population to a target population with differing characteristics.
method A robust sample weighting framework using a reproducing kernel Hilbert space to balance covariates and improve ITR learning methods.
result Improves ITR estimation for the target population compared to other weighting methods.
Bayesian networks with hidden variables help identify causal relationships obscured by confounding.
problem Identifying causal relationships obscured by unobserved confounders.
method Use finite k-mixtures of Bayesian networks with hidden variables to recover the joint probability distribution and identify causal relationships. result First algorithm to learn mixtures of non-empty DAGs, recovering identifiable causal relationships.
The study provides a theory for causal machine learning with generalization bounds.
problem Lack of theoretical guarantees for causal machine learning algorithms.
method Introduces a novel change-of-measure inequality to bound model loss.
result Tight bounds on model loss in terms of treatment propensities deviation.
The method of covariate adjustment is often used for estimation of population average treatment effects in observational studies. Graphical rules for determining all valid covariate adjustment sets from an assumed causal graphical model are well known. Restricting attention to causal linear models, a recent article der…
Generative models improve causal effect estimation from observational data.
problem Estimating causal effects from observational data, especially when confounding factors are present.
method Proposes a progressive sequence of Variational Auto-Encoder models to learn underlying factors and causal effects.
result Empirical results show superior performance compared to state-of-the-art approaches.
The paper discusses selecting predictive models for causal inference, highlighting the challenges and proposing a solution.
problem Selecting the best predictive models for causal inference from a variety of machine learning models.
method The paper proposes using Rext−risk, flexible estimators, and splitting data to compute risks for model selection. result The proposed method controls both outcome errors for treated and non-treated individuals, addressing the issue of model selection for causal inference.
Improves causal inference with observational data by balancing features and weights.
problem Achieving balance in predictive features for causal inference with observational data.
method Integrates balancing weights into representation learning for causal learning.
result Developed an algorithm for accurate estimation of causal effects.
New federated method preserves privacy and estimates treatment effects.
problem Privacy-preserving causal inference for multi-site studies.
method Multiply robust nuisance function estimation, transfer learning.
result Efficient and optimal treatment effect estimation under different scenarios.
The paper examines challenges in achieving fair predictions using causal counterfactuals.
problem Achieving fair predictions using causal counterfactuals in fairness settings.
method Analyzes the limitations of causal models in fairness settings and the challenges of selecting counterfactuals.
result Causal models that capture counterfactuals are outside the class commonly considered in fairness literature.
Causal graph aids observational study insights in aSAH patients.
problem Lack of clear objectives and tools for identifying necessary adjustments in observational studies.
method Uses causal directed acyclic graphs (DAGs) to provide insights mid-study and identify necessary data enhancements.
result Midway insights and necessary data enhancements identified for meaningful causal questions.
We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) p…
BICauseTree improves causal effect estimation by identifying clusters and balancing treatment allocation.
problem Improving interpretability and transparency in causal effect models from observational data.
method Hierarchical bias-driven stratification using decision trees with a customized objective function.
result BICauseTree provides interpretable causal effect estimation and is comparable to existing methods.
Study uses Wasserstein distance to identify causal orders and unmix sources.
problem Identifying causal relationships and separating sources in non-Gaussian data.
method Wasserstein distance for non-Gaussianity, linear ICA, causal inference.
result Exact identification of ICA unmixing matrix and causal orders.
Method estimates heterogeneous causal effects on networks using orthogonal learning.
problem Challenges in estimating causal effects on networks due to treatment effects on both treated and neighbors, and network homophily.
method Two-stage orthogonal learning framework: first stage uses graph neural networks for nuisance components, second stage residualizes and interpretable attention-based model for causal effects.
result Improves heterogeneous effect estimation and supports interpretable analyses.
New method uses predictions to infer causal effects without labeled data.
problem Data labeling costs limit causal inference experiments.
method Prediction-Powered Causal Inferences (PPCI) using conditional calibration and transfer constraints.
result Valid causal inference achieved on experiments with no human annotations.
The paper addresses causal mediation analysis with post-treatment events, proposing robust estimators and efficient methods.
problem Assessing causal mediation in the presence of post-treatment events like noncompliance or clinical events.
method Identifies natural mediation effects for entire populations and principal strata, derives efficient influence functions, and proposes multiply robust estimators.
result Multiply robust estimators are consistent under four types of misspecifications and efficient when all models are correct.
Unified causal inference framework using distribution adaptation.
problem Estimating Average Treatment Effects (ATE) under uncertainty in propensity scores.
method Reframed as domain adaptation problem, using machine learning techniques.
result Joint Robust Estimator (JRE) achieves up to 15% reduction in MSE.