New method learns unbiased treatment representations from structured high-dimensional data.
problem Estimating causal effects from high-dimensional, structured treatments.
method Contrastive learning approach to learn unbiased treatment representations.
result The method identifies causal factors and discards non-causal ones, leading to unbiased causal effect estimates.
TV-SurvCaus improves causal inference for dynamic treatments in survival analysis.
problem Estimating causal effects of time-varying treatments on survival outcomes.
method Representation balancing techniques extended to time-varying treatment regimes with survival outcomes.
result TV-SurvCaus outperforms existing methods in estimating individualized treatment effects with time-varying covariates and treatments.
New method learns low-dimensional representations of AI-generated treatments.
problem Representing AI-generated treatments without losing semantic meaning.
method Double kernel representation learning with alternating minimization.
result Efficiently learned representations guide generative models and facilitate adaptive online experiments.
Improves representation learning for individual treatment effect estimation.
problem Estimating individual treatment effects with high accuracy.
method Introduces a structure keeper to maintain correlation between baseline covariates and representations, trains a discriminator to balance representation and information loss.
result Proposed SMRL algorithm minimizes treatment estimation error and outperforms state-of-the-art methods.
Neural network feature optimization for causal inference.
problem Estimating heterogeneous treatment effects from data.
method Genetic algorithm optimization of intermediate neural network layers for feature representations.
result Retains useful features for outcome prediction even if related to treatment assignment.
FSRM method improves treatment effect estimation from observational data.
problem Estimating treatment effects from observational data with missing counterfactual outcomes and selection bias.
method FSRM method based on deep representation learning and matching, which maps covariate space into a selective, nonlinear, and balanced representation space.
result FSRM method outperforms state-of-the-art methods in estimating treatment effects.
Paper tackles CATE estimation with missing treatment info.
problem Challenges in estimating CATE with missing treatment information.
method Developed MTRNet, a novel CATE estimation algorithm using domain adaptation.
result Improves CATE estimation over state-of-the-art methods.
NCoRE learns counterfactual representations for combined treatments.
problem Estimating individual response to multiple simultaneous interventions.
method Neural conditional representation with modulators for cross-treatment interactions.
result NCoRE significantly outperforms existing methods in counterfactual treatment effect estimation.
CRN model estimates treatment effects over time using adversarial balancing.
problem Estimating treatment effects over time in medical settings.
method Adversarial domain balancing to remove time-varying confounders.
result CRN achieves lower error in estimating counterfactuals and treatment timing.
The paper proposes a method to precisely decompose confounders and estimate treatment effects.
problem Estimating treatment effects from observational data with confounder identification and balancing.
method Learning decomposed representations to identify and balance confounders and non-confounders.
result The method achieves more precise treatment effect estimation than existing methods.
AACE learns treatment policies from EHRs using annotations to improve accuracy.
problem Learning treatment policies from multimodal EHRs with bias and inefficiency.
method Annotation-assisted coarsened effects (AACE) method.
result AACE outperforms existing methods in predicting treatment benefit from multimodal EHRs.
GraphITE estimates individual effects of graph-structured treatments.
problem Estimating individual effects of complex treatment structures.
method Graph neural networks and Hilbert-Schmidt Independence Criterion regularization.
result GraphITE outperforms baselines in estimating treatment effects for large numbers of treatments.
Proposes Infomax and Domain-Independent Representations for robust causal inference.
problem Handling treatment selection bias and domain imbalance in causal inference with real-world data.
method Utilizes mutual information to learn domain-invariant representations that maximize predictive common information.
result Achieves state-of-the-art performance on causal effect inference across various data distributions.
New DR method improves robustness in high-dimensional treatment effects.
problem Estimating dynamic treatment effects with high-dimensional confounders.
method Proposes a novel DR representation for intermediate conditional outcome models.
result Achieves superior robustness guarantees with high-dimensional confounders.
Intact-VAE estimates treatment effects with latent confounders.
problem Estimating treatment effects under unobserved confounding.
method Intact-VAE, a VAE variant, models latent confounders to identify treatment effects.
result Intact-VAE is a consistent estimator of treatment effects under certain settings.
Paper proposes MIM-DRCFR to learn disentangled factors for better treatment effect estimation.
problem Learning disentangled factors precisely for individual-level treatment effect estimation.
method Multi-task learning framework with MI minimization criteria.
result MIM-DRCFR outperforms state-of-the-art methods in treatment effect estimation.
We link disjoint longitudinal data for rare disease patients using latent representations and mixed-effects regression.
problem Analyzing treatment switches in rare diseases with limited data and changing measurement instruments.
method We embed item values into a shared latent space using variational autoencoders and apply mixed-effects regression to quantify treatment effects.
result Our approach allows for statistical inference and quantifies the impact of treatment switches in spinal muscular atrophy.
Combines observational and randomized data to estimate treatment effects.
problem Estimating heterogeneous treatment effects using only observational data is biased.
method Two-step framework: learn shared structure from observational data, then data-specific structures from randomized data.
result Combining observational and randomized data improves treatment effect estimation.
New methods combine AI with traditional stats for better treatment effect estimation.
problem Estimating treatment effects in large, complex data.
method Combining traditional and advanced machine learning techniques.
result Advanced machine learning improves treatment effect estimation.
Estimating individual level treatment effects (ITE) from observational data is a challenging and important area in causal machine learning and is commonly considered in diverse mission-critical applications. In this paper, we propose an information theoretic approach in order to find more reliable representations for e…
The paper provides bounds and methods for estimating causal effects from observational data.
problem Estimating individual-level causal effects from non-experimental data.
method Generalization bounds, sample re-weighting, representation learning algorithms.
result The proposed methods reduce treatment group distances and improve estimation accuracy.
A new method corrects weight values to improve treatment effect estimation.
problem Estimating heterogeneous treatment effects in high-dimensional data with sample selection bias.
method Differentiable Pareto-Smoothed Weighting (DPSW) framework.
result Our method outperforms existing methods in treatment effect estimation.
Learning causal effects from observational data greatly benefits a variety of domains such as health care, education and sociology. For instance, one could estimate the impact of a new drug on specific individuals to assist the clinic plan and improve the survival rate. In this paper, we focus on studying the problem o…
New method for estimating treatment effects without complex propensity models.
problem Estimating treatment effects in dynamic treatment regimes.
method Recursive Riesz representer estimation for de-biasing corrections.
result Directly estimates de-biasing corrections without auxiliary models.
A method combines deep learning and G-estimation for causal mediation analysis.
problem Estimating structural mediation parameters under unmeasured confounding.
method UNIT method using TARNet for representation learning and G-estimation.
result Improved precision of structural parameter estimator through better representation learning.
Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Counterfactual inference enables one to answer "What if...?" questions, such as "What would be the outcome if we gave this patient treatment $t_…
Proposes a method to estimate treatment effects using instruments.
problem Estimating treatment effects from observational data is challenging when unconfoundedness is violated.
method Leverages instruments to estimate bounds on conditional average treatment effect (CATE) through a mapping to a discrete representation space and a two-step procedure.
result Demonstrates theoretical validity and reduced estimation variance in finite-sample settings.
Unified framework for counterfactual survival analysis improves treatment effect estimation.
problem Limited methods for counterfactual inference with survival outcomes.
method Unified framework for survival outcomes, nonparametric hazard ratio metric.
result Significantly outperforms alternatives in survival-outcome prediction and treatment-effect estimation.
Knowledge-based planning (KBP) is an automated approach to radiation therapy treatment planning that involves predicting desirable treatment plans before they are then corrected to deliverable ones. We propose a generative adversarial network (GAN) approach for predicting desirable 3D dose distributions that eschews th…
A new VAE model identifies and estimates treatment effects with limited overlap.
problem Identifying and estimating treatment effects when subjects with certain features belong to a single treatment group.
method Developed a latent variable model to estimate a prognostic score, which is sufficient for treatment effects. The model is a new type of VAE called β-Intact-VAE.
result The model identifies individualized treatment effects and provides TE error bounds.
This paper develops explainable treatment policies for RPM using clinical knowledge.
problem Barriers to adoption of DHIs and lack of interpretability in purely black-box algorithms.
method Developed a pipeline for learning explainable treatment policies using clinician-informed representations.
result Policies learned from clinician-informed representations are more efficacious and efficient than black-box policies.
Framework integrates mental disorder measurements for personalized treatment.
problem Optimizing treatment for mental disorders with latent mental states and heterogeneity.
method Measurement theory and multi-layer neural network for complex treatment effects.
result Learned treatment policies outperform alternatives on heterogeneous treatment effects.
New algorithm improves treatment effect estimation from observational data.
problem Estimating the benefits and harms of interventions from observational data.
method Develops a deep kernel regression algorithm and posterior regularization framework.
result Substantially outperforms state-of-the-art on various benchmarks datasets.
Study improves drug synergy prediction using ensemble learning.
problem Predicting drug synergy in complex diseases.
method Investigated different compound representations and proposed an ensemble model.
result Ensemble model outperforms baseline models.
New method uses latent variables to estimate treatment effects from single-arm trials.
problem Estimating treatment effects from single-arm trials due to lack of external control groups.
method Latent-variable modeling with amortized variational inference for patient matching and direct effect estimation.
result Improved performance in direct treatment effect estimation and effect estimation via patient matching compared to previous methods.
CFR-Pro enhances treatment effect estimation by incorporating local proximity.
problem Treatment selection bias in HTE estimation from observational data.
method Proximity-enhanced CounterFactual Regression (CFR-Pro) with pair-wise proximity regularizer and subspace projector.
result Significantly outperforms competitors in HTE estimation accuracy.
NICE learns a representation to avoid bad controls in causal inference.
problem Avoiding bad controls in causal inference from observational data.
method Uses invariant risk minimization (IRM) to learn a representation of covariates that avoids bad controls.
result NICE outperforms adjusting for all covariates in cases with unknown collider variables and bad controls.
Proposes a method to estimate personalized treatments from high-dimensional data.
problem Estimating individualized treatment regimes (ITRs) from high-dimensional covariates.
method Directly targets the contrast between potential outcomes, using dimension-reduced outcome-weighted learning.
result Achieves universal consistency, converging to the Bayes risk under mild conditions.
Proposes a new VAE model to estimate treatment effects from confounded data.
problem Estimating treatment effects in the presence of confounding variables.
method Intact-VAE, a variant of variational autoencoder (VAE), using a latent variable for confounders.
result Proves identification of treatment effects under unconfoundedness and shows state-of-the-art performance.
Estimating treatment effects in time series with hidden confounding.
problem Estimating treatment effects in time series with hidden confounding.
method A neural framework that learns individual-level counterfactuals and flexible matching procedures.
result Improves counterfactual estimation under latent bias.
Proposes bounds on bias from low-dimensional representations in CATE estimation.
problem Bias in CATE estimation due to low-dimensional representations.
method Proposes a refutation framework to estimate bounds on representation-induced confounding bias.
result Demonstrates effectiveness of refutation framework in practice.
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are ei…
SurvITE learns treatment effects from time-to-event data, addressing unique challenges.
problem Inferring heterogeneous treatment effects from time-to-event data.
method Proposes a novel deep learning method for treatment-specific hazard estimation.
result Method outperforms baselines by addressing covariate shifts from various sources.
SurvCaus improves survival CATE estimation using neural nets.
problem Estimating Individual Treatment Effects (ITE) in survival analysis.
method Representation balancing for counterfactual inference with neural networks.
result The proposed method outperforms baseline methods in synthetic and semisynthetic datasets.
The paper addresses causal estimation for text data with apparent overlap violations.
problem Estimating causal effects from text data with unknown confounders and apparent overlap.
method Uses supervised representation learning to create a representation that preserves confounding information while eliminating predictive information, satisfying overlap assumptions.
result Shows how to obtain robust causal estimation in the presence of apparent overlap violations.
Q-learning adapted to find near-equivalent treatment strategies.
problem Finding optimal treatment sequences in dynamic treatment regimes.
method Introducing a worst-value tolerance criterion to find sets of near-equivalent policies.
result Constructs families of near-equivalent treatment strategies.
EBM reduces dimensionality for estimating heterogeneous CATEs.
problem Estimating CATEs requires many confounding variables, increasing sample complexity.
method Proposes an EBM that learns a low-dimensional representation of variables.
result EBM representations keep CATE estimates consistent and perform better than other methods.
MOCA uses modular attention to estimate causal effects from complex data.
problem Estimating causal effects from observational data with complex, non-linear, and high-dimensional treatment and outcome mechanisms.
method MOCA is a transformer-based framework that separates treatment and outcome modeling through modular design and one-way attention mechanism, with cutting-feedback to prevent outcome influence on treatment representations.
result MOCA outperforms classical estimators and machine learning approaches across various simulated and real-world scenarios.