Method for understanding heterogeneous treatment effects in complex causal graphs.
problem Heterogeneity and comorbidity in healthcare problems.
method Developed a new approach to characterize heterogeneous causal effects (HCEs) in graphical contexts, including heterogeneous causal graphs (HCGs) with confounders and mediators.
result Established theoretical forms and properties of HCEs in linear and nonlinear models, and developed interactive structural learning for estimation.
Paper identifies and estimates CAPCEs in continuous treatment settings.
problem Estimating heterogeneous causal effects of continuous treatments.
method Instrumental variable approach to identify CAPCEs under weaker conditions.
result Developed three families of CAPCE estimators with statistical properties analyzed.
Study estimates heterogeneous principal causal effects with binary treatments and intermediate variables.
problem Estimating subgroup effects within strata defined by potential values of an intermediate variable.
method Proposes a framework for estimating and forming confidence intervals for heterogeneous principal causal effects under principal ignorability assumption. Develops several estimators with varying robustness properties.
result Established large-sample theory and analyzed bias contributions of each approach.
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 quantifies variable importance in causal forests for treatment effect heterogeneity.
problem Lack of understanding how input variables affect treatment effect heterogeneity in causal forests.
method Developed a new importance variable algorithm for causal forests based on the drop and relearn principle.
result Shows how to handle forest retraining without a confounding variable and introduces a corrective term for confounders.
A framework for causal classification using uplift and causal heterogeneity methods.
problem Predicting the effect of interventions on different individuals from data.
method Causal classification framework using off-the-shelf supervised methods.
result Framework works for causal classification and uplift modelling, competitive with other methods.
This paper introduces an innovative Bayesian machine learning algorithm to draw interpretable inference on heterogeneous causal effects in the presence of imperfect compliance (e.g., under an irregular assignment mechanism). We show, through Monte Carlo simulations, that the proposed Bayesian Causal Forest with Instrum…
Causalfe estimates treatment effects in panel data with fixed effects.
problem Spurious heterogeneity in treatment effect estimates due to fixed effects in panel data.
method CFFE approach with node-level residualization during tree construction.
result Validates the estimator's performance through simulation studies.
Blog post comparing neural network methods for causal inference.
problem Estimating heterogeneous treatment effects in causal inference.
method Developed and compared a fully connected neural network implementation of Bayesian Causal Forest.
result Improvements in performance in simulation settings.
CausalMix generates synthetic data with causal controls for mixed-type tables.
problem Synthetic data for causal inference with mixed-type and multimodal tabular data.
method CausalMix combines Gaussian latent priors with data-type-specific decoders for control over overlap, confounding, and treatment effect heterogeneity.
result CausalMix achieves state-of-the-art distributional metrics and stable causal control.
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.
Proposes CoDEAL for estimating heterogeneous treatment effects in panel data models.
problem Estimating heterogeneous treatment effects in causal panel data models with covariate effects.
method Covariate-Adjusted Deep Causal Learning (CoDEAL) integrating neural networks and autoencoders.
result Establishes theoretical guarantees and demonstrates compelling performance in simulations and real data.
Study identifies and estimates treatment effect heterogeneity within principal stratification subpopulations.
problem Causal inference with intermediate outcomes and treatment effect heterogeneity.
method Proposes a novel doubly cross-fit doubly robust machine learner to efficiently learn conditional principal causal effects under principal ignorability.
result Demonstrates informative patterns of treatment effect heterogeneity within the always-survivor subpopulation in an acute lung injury trial.
Proposes new method for calibrating treatment effect predictors.
problem Calibrating predictors of heterogeneous treatment effects.
method Causal isotonic calibration and cross-calibration.
result Achieves fast calibration rates under weak conditions.
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.
Adaptive kernel approach learns causal effects from diverse data sources.
problem Learning causal effects from multiple, decentralized data sources in a federated setting.
method Adaptive transfer algorithm using Random Fourier Features to estimate similarities and disentangle loss function components.
result Empirically outperforms baselines on decentralized data sources with different distributions.
Develops a sparsity-inducing Bayesian Causal Forest for estimating heterogeneous treatment effects.
problem Estimating heterogeneous treatment effects using observational data with varying degrees of sparsity.
method Introduces a sparsity-inducing version of Bayesian Causal Forests with additional priors to adjust covariate weights.
result Improves adaptability to sparse data generating processes and uncovering moderating factors driving heterogeneity.
This paper uses machine learning to estimate how different types of crashes affect highway traffic.
problem Estimating the heterogeneous causal effects of crashes on highway traffic.
method Neyman-Rubin Causal Model, Conditional Shapley Value Index, Structural Causal Model, Doubly Robust Learning.
result Different types of crashes have varying impacts on traffic, with rear-end crashes causing the most severe congestion.
New method targets relative risk heterogeneity in clinical trials.
problem Identifying treatment effects across subgroups with absolute risk differences.
method Modified causal forests using a novel node-splitting procedure based on relative risk.
result Relative risk causal forests can capture heterogeneity not detected by absolute risk methods.
Direct learning framework for integrating multi-source causal data.
problem Conditional average treatment effects inference from heterogeneous data.
method Direct learning framework, double robustness, causal information-aware weighting function.
result Effective causal data fusion in both homogeneous and heterogeneous scenarios.
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.
Bayesian Causal Forest models estimate treatment effects with noncompliance.
problem Estimating treatment effects with noncompliance and varying compliance rates.
method Bayesian Causal Forest model for binary response variables.
result Flexibly estimate heterogeneous treatment effects among compliers.
Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers. This paper develops new estimation and inference procedures for multiple treatment models in a selection-on-observables framework by modifying the Causal Fore…
New Random Forest variants estimate heterogeneous treatment effects using Wasserstein distances.
problem Estimating heterogeneous treatment effects in complex situations.
method Proposes natural variants of Random Forests using Wasserstein distances.
result Natural variants of Random Forests are well-suited for estimating conditional distributions.
Bayesian approach for estimating heterogeneous treatment effects in RDD designs.
problem Heterogeneity in treatment effects in RDD designs can lead to misleading conclusions.
method Direct Bayesian Additive Regression Trees (BART) for modeling heterogeneous treatment effects.
result Flexibly captures complicated structures of heterogeneous treatment effects as a function of covariates.
CRL approach improves understanding of heterogeneous treatment effects in complex diseases.
problem Estimating heterogeneous treatment effects in complex diseases.
method Causal rule learning (CRL) workflow consisting of rule discovery, selection, and analysis.
result CRL outperforms other methods in providing interpretable estimates of HTE.
This paper provides a link between causal inference and machine learning techniques - specifically, Classification and Regression Trees (CART) - in observational studies where the receipt of the treatment is not randomized, but the assignment to the treatment can be assumed to be randomized (irregular assignment mechan…
New method uses kernel deviance measures to discover causal relationships in heterogeneous data.
problem Discovering causal relationships in complex, heterogeneous datasets.
method KIIM-HT, a novel score measure based on heterogeneous transformations of RKHS embeddings.
result KIIM-HT outperforms previous methods in causal discovery tasks.
The paper proposes a method to estimate heterogeneous treatment effects using pretraining strategies.
problem Estimating conditional average treatment effects (CATE) in the presence of many covariates.
method The approach leverages prognostic factors that also predict treatment effect heterogeneity, using the R-learner framework.
result The proposed method improves estimation accuracy and power for detecting treatment effect heterogeneity.
Spatially-aware model improves earthquake hazard assessment accuracy.
problem Misrepresentation of seismic effects across diverse landscapes.
method Causal Bayesian network with Gaussian Processes and normalizing flows.
result Achieves up to 35.2% AUC improvement over existing methods.
We address the problem of inferring the causal effect of an exposure on an outcome across space, using observational data. The data is possibly subject to unmeasured confounding variables which, in a standard approach, must be adjusted for by estimating a nuisance function. Here we develop a method that eliminates the …
Expands causal clustering framework with hierarchical and density-based methods.
problem Identifying heterogeneous treatment effects in unknown subgroup structure.
method Integrates hierarchical and density-based clustering algorithms into causal k-means clustering.
result Plug-in estimators for causal clustering are simple and readily implementable.
Many scientific and engineering challenges -- ranging from personalized medicine to customized marketing recommendations -- require an understanding of treatment effect heterogeneity. In this paper, we develop a non-parametric causal forest for estimating heterogeneous treatment effects that extends Breiman's widely us…
The paper proposes a new evaluation framework for causal inference models.
problem Challenges in estimating causal effects from observational data.
method Complements evaluation of causal inference models with statistical evidence and non-parametric tests.
result Eliminates the influence of a few instances or simulations on benchmarking results.
Deep learning aids causal inference in complex settings.
problem Estimating heterogeneous treatment effects in non-linear, time-varying, and encoded confounders.
method Intuitive introduction to deep learning and causal inference, focusing on observational data.
result Maximizes accessibility to causal inference through deep learning.
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in a survival and observational setting where outcomes may be right-censored. Our app…
There is an increasing interest in estimating heterogeneity in causal effects in randomized and observational studies. However, little research has been conducted to understand heterogeneity in an instrumental variables study. In this work, we present a method to estimate heterogeneous causal effects using an instrumen…
DEBIAS learns causal effects from psychiatric longitudinal data by optimizing outcome weights.
problem Causal inference challenges in psychiatric longitudinal data due to symptom heterogeneity and latent confounding.
method DEBIAS algorithm that optimizes outcome weights to maximize durable treatment effects and minimize confounding.
result DEBIAS consistently outperforms state-of-the-art methods in recovering causal effects for clinically interpretable composite outcomes.
Proposes P-learner for estimating treatment effects with proxy variables.
problem Estimating treatment effect heterogeneity in settings with unverifiable exchangeability.
method Two-stage loss function for learning heterogeneous treatment effects with proxy variables.
result P-learner satisfies an oracle bound on estimated error.
GCF estimates heterogeneous treatment effects for continuous treatments in online marketplaces.
problem Estimating heterogeneous treatment effects for continuous treatments in online marketplaces.
method Kernel-based doubly robust estimator and distance-based splitting criterion.
result GCF estimates heterogeneous treatment effects for continuous treatments effectively.
This research tackles sample complexity in causal graph recovery with temporal heterogeneity.
problem Recovering a unique causal graph from observational data with temporal heterogeneity.
method Integrates time-series dynamics and multi-environment heterogeneity to constrain the problem, enabling a rigorous analysis of statistical limits.
result Unified necessary identifiability conditions and explicit information-theoretic bounds quantify the sample complexity under different noise distributions.
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.
DAG-FM discovers causal relationships from heterogeneous data.
problem Challenges in causal discovery from heterogeneous causal mechanisms.
method DAG-FM uses two specialized Transformer-based sub-modules and a robust tabular interaction block to model complex row-column interactions.
result DAG-FM achieves state-of-the-art performance on synthetic and real-world datasets.
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.
FOCaL meta-learner estimates functional treatment effects robustly.
problem Estimating heterogeneous treatment effects from functional outcomes.
method Doubly robust meta-learner FOCaL integrating functional regression.
result Direct and robust estimation of F-CATE.
New methods for estimating treatment effects with missing data.
problem Missing outcome data complicates estimating treatment effects.
method Proposed two de-biased machine learning estimators (mDR-learner and mEP-learner) to address under-representation.
result Oracle efficiency of the proposed estimators under reasonable conditions.
A framework uses proxies to prioritize treatment without estimating causal effects.
problem Prioritizing treatment when causal effects are hard to estimate.
method Decision-focused framework identifying conditions for proxy usefulness.
result Proxies can recover correct effect ordering under specific conditions.
New insights show Medicaid impacts on ED use vary widely, with some groups seeing significant increases.
problem Understanding the varied impacts of Medicaid on emergency department use.
method Causal machine learning methods to identify heterogeneous impacts.
result Meaningful heterogeneity in the effect of Medicaid on ED use, with a small group driving the overall effect.