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.
Estimates the effect of time-varying treatments using machine learning.
problem Estimating the impact of time-varying treatments over multiple periods.
method Difference-in-Differences framework with double/debiased machine learning.
result Higher vaccination rates reduce COVID-19 mortality after several weeks.
TCFimt forecasts causal effects of multiple interventions from individual data.
problem Estimating causal effects of temporal multi-interventions from individual data.
method TCFimt uses adversarial tasks in seq2seq framework to alleviate bias and contrastive learning to decouple effects.
result TCFimt outperforms state-of-the-art methods in predicting future outcomes and choosing optimal treatments.
Proposes DSW for unbiased ITE estimation with dynamic confounders.
problem Estimating ITE from dynamic observational data with time-varying confounders.
method Deep Sequential Weighting (DSW) infers hidden confounders using current treatment assignments and historical information.
result DSW generates unbiased and accurate treatment effects.
CAST models time-varying treatment effects in cancer patients.
problem Estimating treatment effects at fixed time points limits understanding of dynamic changes over time.
method CAST combines parametric and non-parametric methods to model continuous time-varying treatment effects.
result CAST reveals how treatment effects rise, peak, and decline over the follow-up period.
A new method generates counterfactual treatment outcomes for time-varying treatments.
problem Estimating counterfactual outcomes for time-varying treatments with high-dimensional outcomes.
method Conditional generative framework with inverse probability re-weighting.
result Our method outperforms state-of-the-art baselines in generating high-quality counterfactual samples.
New method estimates causal effects with multi-valued, time-varying treatments.
problem Estimating causal effects with complex time-varying exposures.
method Combines machine learning and semiparametric efficiency theory.
result Proposes an efficient, asymptotically normal estimator for marginal structural models.
Proposes estimators for complex dose-response curves using kernel methods.
problem Estimating complex dose-response curves with continuous treatments, mediators, and covariates.
method Kernel ridge regression with sequential kernel embedding technique.
result Simple estimators for mediated and time-varying dose response curves with nonasymptotic uniform rates.
New meta-learners estimate time-varying treatment effects without model assumptions.
problem Estimating treatment effects over time in personalized medicine.
method Model-agnostic meta-learners for weighted pseudo-outcome regressions.
result Comprehensive theoretical analysis and practical insights for choosing meta-learners.
Method constructs prediction intervals for time-varying individual treatment effects.
problem Accurately quantify uncertainty of individual treatment effects across multiple decision points.
method Conformal inference techniques for time-varying ITEs with weaker assumptions.
result Guaranteed lower bound for coverage dependent on data non-exchangeability.
Proposes a model to estimate treatment effects in complex multiagent systems over time.
problem Challenges in evaluating interventions in multiagent systems, especially with time-varying relationships and covariates.
method Interpretable counterfactual recurrent network leveraging graph variational recurrent neural networks and domain knowledge.
result Achieved lower estimation errors and more effective treatment timing than baselines in simulated and real-world scenarios.
DeepBlip estimates treatment effects over time using neural networks.
problem Estimating treatment effects over time with interpretable blip effects.
method DeepBlip uses a novel double optimization trick to enable simultaneous learning of blip functions with sequential neural networks.
result DeepBlip achieves state-of-the-art performance across various clinical datasets.
DSL estimates heterogeneous treatment effects over time in survival settings.
problem Complicated by right censoring and time-varying treatment effects.
method Deep survival learner (DSL) for estimating CATEs over a clinically relevant time spectrum.
result DSL reveals heterogeneity in perioperative chemotherapy effects over time.
Study tackles causal effects of close contact on MRSA infections from entangled treatment data.
problem Estimating causal effects of close contact on MRSA infections from observational data with entangled treatments.
method NEAT method that models treatment assignment mechanism and mitigates confounding biases.
result NEAT method effectively estimates causal effects from entangled treatment data.
Dynamic treatment effects estimated over time using covariate balancing.
problem Estimating treatment effects in panel data with dynamic treatments.
method Dynamic covariate balancing with potential local projections.
result Established inferential guarantees for the proposed method.
New method estimates treatment effects over time for survival data, improving accuracy and smoothness.
problem Estimating treatment effects over time for survival data with left truncation and right censoring.
method surv-iTMLE, a targeted learning procedure for estimating conditional survival probabilities.
result surv-iTMLE outperforms existing methods in bias and smoothness of time-varying effect estimates.
G-Net uses deep learning for complex counterfactual outcome prediction.
problem Estimating counterfactual outcomes under dynamic treatment strategies.
method G-Net is a sequential deep learning framework for G-computation.
result G-Net can handle complex temporal data and provide accurate treatment effects.
Develops algorithm to find subgroups with different treatment effects in HIV patients.
problem Estimating treatment effects in EHR data with challenges like time-varying confounding.
method SDLD algorithm combining generalized interaction tree and longitudinal targeted maximum likelihood estimation.
result Identifies subgroups of HIV patients at higher risk of weight gain with dolutegravir-containing ARTs.
The estimation of treatment effects is a pervasive problem in medicine. Existing methods for estimating treatment effects from longitudinal observational data assume that there are no hidden confounders, an assumption that is not testable in practice and, if it does not hold, leads to biased estimates. In this paper, w…
The paper uses double machine learning to estimate dynamic treatment effects robustly.
problem Estimating causal effects of dynamic treatments with time-varying covariates.
method Double machine learning with Neyman-orthogonal score functions for robustness.
result Asymptotic normality and n \sqrt{n} n -consistency of the estimators under specific conditions. Estimates causal effects from patient trajectories using DeepACE model.
problem Estimating causal effects from observational data in medical practice.
method DeepACE model using iterative G-computation formula and sequential targeting procedure.
result DeepACE achieves state-of-the-art performance in estimating time-varying ACEs.
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.
Proposes a method for interpreting time-varying causal effect moderation in high-dimensional data.
problem Interpreting causal effect moderation in high-dimensional data with interpretability and avoiding false positives.
method Two-step method: 1) Selects a smaller model for linear causal effect moderation using Gaussian randomization, 2) Conditions on selection to construct a pivot for uniformly asymptotic semi-parametric inference.
result Consistently achieves valid coverage rates and shorter, bounded intervals in time-varying causal effect moderation.
Develops framework for estimating and improving DTRs with time-varying IV in the presence of unmeasured confounding.
problem Estimating DTRs from observational data with unmeasured confounding.
method Time-varying instrumental variable (IV) framework for estimating and improving DTRs.
result IV-optimal and IV-improved DTRs perform better than DTRs assuming no unmeasured confounding.
Develops methods for causal inference in longitudinal data.
problem Estimating Individual Treatment Effects (ITEs) in high-dimensional, time-varying data.
method Causal Dynamic Variational Autoencoder (CDVAE) and long-term counterfactual regression framework.
result CDVAE outperforms baselines and improves state-of-the-art models, approaching oracle performance.
Develops methods to learn optimal treatment regimes using causal tree methods.
problem Lack of methods for estimating treatment effects and handling complex patient data.
method Causal tree and causal forest methods for estimating heterogeneous treatment effects.
result Outperforms state-of-the-art baselines in cumulative regret and percentage of optimal decisions.
Paper develops a new estimator for dynamic treatment effects in high-dimensional settings.
problem Time-varying confounding and model misspecification in estimating dynamic treatment effects.
method Sequential model doubly robust estimator with moment-targeting estimates.
result Root-N inference achieved under model misspecification, even with high-dimensional covariates.
Estimates RL data for dynamic treatment effects using GMM.
problem Estimating dynamic treatment effects from RL data with nonstationary behavior policies.
method Weighted GMM approach to stabilize variance in adaptive RL settings.
result Valid hypothesis testing and confidence regions for dynamic treatment effects.
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.
Proposes methods for learning optimal dynamic treatment regimes robust to unconfoundedness violations.
problem Estimating optimal dynamic treatment regimes using historical observational data when unconfoundedness is violated.
method Utilizes proximal causal inference framework to propose three nonparametric identification methods, a (K+1)-robust method, and establish a semiparametric efficiency bound.
result Establishes the (K+1)-robust method for learning optimal dynamic treatment regimes, validating its efficiency and multiple robustness through numerical experiments.
Bayesian method corrects timing misalignment in recurrent event studies.
problem Estimating differences in event rates under two treatments with timing misalignment.
method g-computation procedure with joint semiparametric Bayesian model.
result Correctly estimates average causal effects under right-censoring.
Marginal structural models (MSMs) estimate the causal effect of a time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approach of using inverse probability of treatment weighting (IPTW) can lead to high-variance estimates due to extreme weights and be sensitive to …
Meta-learning method for estimating time-varying mHealth intervention effects.
problem Complex mHealth data and uncertain randomization probabilities.
method DR-WCLS meta-learning procedure for causal excursion effects.
result More efficient and consistent estimates of causal excursion effects.
Bayesian model estimates ACT impact on pediatric AML survival.
problem Estimating ACT impact on survival in AML patients with treatment timing issues.
method Generative Bayesian semi-parametric model with Gamma Process priors.
result Posterior inference for ACT efficacy under dynamic rules.
Bayesian methods improve causal effect estimation, offering shrinkage and sensitivity analysis.
problem Improving causal effect estimation in practical settings.
method Parametric and nonparametric Bayesian approaches.
result Priors induce shrinkage and sparsity in parametric models.
Deep learning improves causal effect estimation from complex observational data.
problem Estimating causal effects from complex observational data with low bias.
method Unified deep learning framework using multitask recurrent neural networks.
result Deep learning estimator shows lower bias in causal effect estimates.
New methods for estimating complex causal effects in econometrics.
problem Estimating causal parameters in short panel data models using nested nonparametric instrumental variable regression.
method Introducing techniques to limit ill-posedness in nested NPIV, providing explicit mean square rates and efficient inference.
result Explicit mean square rates for nested NPIV and efficient inference for causal parameters.
MSCT predicts post-crash traffic speed using causal inference.
problem Time-varying confounding bias in post-crash traffic prediction.
method Marginal Structural Causal Transformer (MSCT) incorporating Marginal Structural Models and balanced loss function.
result MSCT outperforms state-of-the-art models in multi-step-ahead prediction.
Developed a flexible Bayesian g-formula for causal survival analysis with time-dependent confounding.
problem Estimating causal survival curves in longitudinal observational studies with time-varying treatments and confounding.
method Incorporated Bayesian Additive Regression Trees (BART) into the g-formula to model time-evolving generative components and mitigate bias due to model misspecification.
result Demonstrated improved empirical performance and practical utility of the proposed method through simulations and real-world data analysis.
New method for finding optimal treatment regimes in medical settings with time-varying unobserved factors.
problem Finding optimal treatment regimes in medical settings with time-varying unobserved factors.
method Extend Dynamic Treatment Regimes (DTRs) to Ambiguous Dynamic Treatment Regimes (ADTRs), connect to Ambiguous Partially Observable Mark Decision Processes (APOMDPs), and develop Reinforcement Learning methods.
result Established theoretical results for learning methods, including consistency and asymptotic normality.
CausalLongPFN predicts counterfactual outcomes from time-series data.
problem Predicting future outcomes under varying treatments in time-series data with confounding and heterogeneity.
method Prior-fitted network pretrained on synthetic episodes of temporal structural causal models.
result CausalLongPFN outperforms domain-trained models on factual and counterfactual prediction tasks.
Proposes a deep spectral Q-learning for mobile health data.
problem Personalized treatment assignment for patients with time-varying covariates.
method Integrates PCA with deep Q-learning for mixed frequency data.
result Mean return converges to optimal under estimated optimal policy.
Proposes PE-GP-UCB for time-varying Bayesian optimisation.
problem Time-varying Gaussian process bandits with unknown prior.
method PE-GP-UCB algorithm, relying on consistency of function values with priors.
result Regret bound provided for the proposed algorithm.
M3E2 neural network estimates multiple treatment effects.
problem Estimating effects of multiple treatments simultaneously.
method Multi-task learning neural network model for multiple treatments, continuous and binary.
result M3E2 outperforms baselines in synthetic datasets.
Proposes a new method to estimate continuous treatment policies and match treatments effectively.
problem Current methods struggle with continuous treatment policies and complex matching.
method Formulates treatment effectiveness as a parametrizable model, using deep learning for optimization.
result Significant improvement in treatment effectiveness and matching efficiency.
A new thompson sampling method controls for time-varying effects.
problem Dynamic experiments in online services with time-varying effects.
method Odds-ratio Thompson Sampling
result The proposed method works robust to time-varying effects.
New test for binary treatment effects using kernel methods.
problem Testing distributional effects of binary treatments.
method Kernel-based doubly-robust test, avoiding permutations.
result Valid type-I error with computational efficiency.
XTNet estimates complex cross-treatment effects in multi-category, multi-valued settings.
problem Challenges in estimating causal effects for multi-category, multi-valued treatments.
method Dynamic Neural Masking for capturing treatment interactions without restrictive assumptions.
result XTNet consistently outperforms state-of-the-art baselines in multi-category, multi-valued treatment effect estimation.