Proposes BDCM to handle unmeasured confounders in causal inference.
problem Handling unmeasured confounders in causal inference.
method Backdoor criterion to find variables for diffusion model.
result Captures counterfactual distribution more precisely.
A method to assess sensitivity to unmeasured confounding with sharp bounds.
problem Assessing the impact of unmeasured confounding on causal effects.
method Sets two intuitive parameters to estimate sensitivity intervals.
result Bounds on true causal effects can be tighter than existing methods.
Proposes a method to estimate policy values in reinforcement learning with unmeasured confounders.
problem Estimating policy values in reinforcement learning with unmeasured confounders.
method Develops a two-way deconfounder algorithm using a neural tensor network to learn unmeasured confounders and system dynamics.
result Consistent policy value estimation through model-based estimator.
DCCD-CONF discovers causal graphs with unmeasured confounders.
problem Discovering causal relationships in systems with unmeasured confounders.
method Differentiable learning of nonlinear cyclic causal graphs using interventional data.
result DCCD-CONF outperforms state-of-the-art methods in causal graph recovery and confounder identification.
Develops methods for personalized treatment decisions in the presence of unmeasured factors.
problem Personalized treatment decisions in the presence of unmeasured confounding.
method Proximal learning approaches to estimate optimal individualized treatment regimes (ITRs).
result Established identification results for different classes of ITRs, improving decision-making value function.
Algorithm detects unmeasured confounding in observational data.
problem Estimating treatment effects in observational studies with untestable conditions.
method Two-stage procedure that detects dependencies between causal mechanisms.
result Algorithm efficiently detects confounding on simulated and semi-synthetic data.
The paper tackles individualized decision-making under unmeasured confounding, providing a novel minimax solution and a paradox.
problem Unmeasured confounding in causal inference leads to biased estimates and affects individualized decision-making.
method The authors establish a formal link between individualized decision-making under partial identification and classical decision theory, providing a minimax solution and a paradox.
result A novel minimax solution for individualized decision-making/policy assignment is provided, and an interesting paradox is drawn.
New method identifies causal relationships using proxy variables in the presence of unmeasured confounders.
problem Challenges in inferring causal relationships due to unmeasured confounding.
method Develops a general nonparametric approach using a single negative control outcome (NCO) and negative control exposure (NCE).
result Establishes a new identification result and proposes a kernel-based testing procedure.
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.
Paper tackles RL with continuous actions and unmeasured confounders.
problem Offline policy learning with continuous actions and unmeasured confounders.
method Developed a novel identification result and a minimax estimator for nonparametric policy value estimation.
result Introduced a policy-gradient-based algorithm to identify the optimal policy.
Sharp bounds on ATE with unmeasured confounders, valid even when misspecified.
problem Bounding average treatment effects with unmeasured confounders.
method Distributionally robust optimization, double sharpness, double validity.
result Proposes estimators with robustness properties for valid bounds.
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.
New methods estimate causal effects using front-door criterion in presence of unmeasured confounders.
problem Estimating causal effects in observational studies with unmeasured confounders.
method Developed novel one-step and targeted minimum loss-based estimators for front-door assumptions.
result Established conditions for root-n consistency and asymptotic linearity.
New method learns optimal policies in presence of unmeasured confounders.
problem Optimal policy learning with unobserved confounders.
method Causal-assisted policy learning methods using instrumental variables and negative controls.
result Policies are i l d e O ( n − 1 / 2 ) ilde{\mathscr{O}}(n^{-1/2}) i l d e O ( n − 1/2 ) quantile-optimal under mild coverage assumptions. New method discovers causal relationships in confounded systems.
problem Discovering causal relationships in systems with unmeasured confounding variables.
method Differentiable algebraic constraints for continuous optimization of ADMGs.
result Effective method for causal discovery in confounded linear systems.
Simulation study evaluates causal ML models under confounding violations.
problem Assessing conditional exchangeability in causal machine learning models.
method Simulation study with varying confounding, sample size, and NCO structures.
result Causal ML models fail to recover true treatment effect heterogeneity under violations of conditional exchangeability.
Deep neural networks improve proximal inference for causal effects.
problem Estimating causal effects in the presence of unmeasured confounders.
method Flexible deep neural network to estimate the bridge function.
result Achieves state-of-the-art performance on benchmarks.
New algorithms bound treatment effects with unmeasured confounding.
problem Estimating causal effects when confounding is unmeasured.
method Formulate causal effects as objective functions in optimization, using stochastic methods and Monte Carlo.
result Efficient algorithms for bounded treatment effects in complex settings.
Causal approaches to fairness have seen substantial recent interest, both from the machine learning community and from wider parties interested in ethical prediction algorithms. In no small part, this has been due to the fact that causal models allow one to simultaneously leverage data and expert knowledge to remove di…
There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally be ruled out with certainty in observational studies or randomized tri…
Unified framework for large-scale hypothesis testing with confounders.
problem Bias in large-scale hypothesis testing due to unmeasured confounders.
method Unified statistical estimation and inference framework that disentangles confounding effects and jointly estimates latent and primary effects.
result Effective Type-I error control and power in hypothesis testing.
The paper tackles long-term treatment effects with persistent confounders using sequential short-term outcomes.
problem Estimating long-term treatment effects with persistent unmeasured confounders.
method Exploiting the sequential structure of short-term outcomes, the paper develops three novel identification strategies and corresponding estimators.
result The proposed methods outperform existing approaches in handling persistent confounders.
Framework uses human annotations to make models robust to spurious correlations.
problem Machine learning models fail when unmeasured variables change test distributions.
method Human annotations to augment training examples, UV-DRO objective for robustness.
result Improvements of 5-10% on digit recognition task and 1.5-5% on NYPD Police Stops analysis.
New methods estimate causal effects through mediators, handling confounding without strict assumptions.
problem Estimating causal effects through mediators while accounting for unmeasured confounding.
method Developed four nonparametric identification strategies using proximal confounding bridge functions, efficient influence function, and quadruply robust estimator. Proposed proximal debiased machine learning approach for high-dimensional nuisance parameters.
result Achieved n \sqrt{n} n -consistency and asymptotic normality for path-specific effect estimation. Estimating the individual treatment effect (ITE) from observational data is essential in medicine. A central challenge in estimating the ITE is handling confounders, which are factors that affect both an intervention and its outcome. Most previous work relies on the unconfoundedness assumption, which posits that all th…
Framework sharpens causal effect estimates without external assumptions.
problem Estimating causal effects under unmeasured confounding.
method Information-theoretic divergence bounds, Neyman orthogonality, machine learning.
result Sharp partial identification of conditional causal effects from observational data.
Study on the probability of immunity and its bounds.
problem Estimating the probability of immunity and its bounds.
method Derive necessary and sufficient conditions for non-immunity and ε-bounded immunity; introduce indirect immunity; propose sensitivity analysis.
result Estimate the probability of benefit and produce tighter bounds of the probability of benefit.
Proposes a method to estimate causal effects of continuous treatments using instrumental variables.
problem Estimating causal effects of continuous treatments in the presence of unmeasured confounders.
method Introduces a novel framework using instrumental variables and a uniform regular weighting function to identify and estimate average dose-response functions.
result Establishes the asymptotic properties of the proposed methods for estimating average dose-response functions.
New approach uses negative controls to estimate causal parameters without completeness conditions.
problem Estimating causal parameters when not all confounders are observed.
method Identification strategy based on minimax learning formulations for general function classes.
result Avoids completeness conditions and uniqueness assumptions on bridge functions.
Confounding biases recommender systems, even when data seems fully observed.
problem Unmeasured features influencing both treatment and outcome in recommender systems.
method Illustrations and simulation studies showing how common practices introduce confounding.
result Standard recommender system practices can introduce confounding, reducing performance.
(This comment has been updated to respond to Wang and Blei's rejoinder [arXiv:1910.07320].) The premise of the deconfounder method proposed in "Blessings of Multiple Causes" by Wang and Blei [arXiv:1805.06826], namely that a variable that renders multiple causes conditionally independent also controls for unmeasured mu…
Paper proposes mechanism learning to reverse causal inference in ML.
problem Machine learning models learn associational, not causal, relationships.
method Causally weighted Gaussian mixture models (CW-GMMs).
result CW-GMMs can deconfound observational data for reverse causal inference.
Proposes a method to explain black-box models using causal learning.
problem Existing explainability methods focus on micro-level inputs, not interpretable features.
method Learns causal graphical representations to differentiate between causal and confounding influences.
result Graphs can differentiate between interpretable and confounding features.
New method combines experimental and observational data for causal inference.
problem Combining internal validity of experiments and larger sample sizes of observations.
method Empirical risk minimization (ERM) framework with cross-validation.
result Efficacy and reliability demonstrated on real and synthetic data.
TabCF simplifies causal inference for distributional effects.
problem Estimating causal effects with unmeasured confounding.
method Control function regression using tabular foundation models.
result Accurate, fast, and transparent causal estimation of distributional quantities.
Paper develops IV method for consistent OPE in confounded MDPs.
problem Confounding variables in observational data affect OPE effectiveness.
method Instrumental variable approach for consistent off-policy evaluation.
result IV method enables correct identification of target policy's value.
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 …
Develops a deep survival model for causal inference in longitudinal studies.
problem Estimating treatment effects on time-to-event outcomes in observational studies with time-dependent covariates.
method TCS model using potential outcomes framework and ensemble of recurrent subnetworks.
result Identifies conditional average treatment effects and individual treatment effect heterogeneity over time.
The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have become available, opening up new opportunities for more cost-effective assessments. …
Paper develops a method to estimate value of a policy in confounded MDPs.
problem Estimating value of a policy in the presence of unmeasured confounders.
method Uses auxiliary variables to identify target policy's value in a confounded MDP.
result Develops an off-policy value estimator robust to model misspecification.
Sensitivity analysis for individualized effects in OTRs with binary risk factors.
problem Addressing omitted confounding in individualized effects of OTRs.
method Simulation-based sensitivity analysis to simulate unmeasured confounders.
result Benchmarking the strength of omitted confounding for binary risk factors.
Debiased learners estimate heterogeneous treatment effects in observational studies.
problem Estimating heterogeneous treatment effects in observational studies with unmeasured confounders.
method Debiased Front-Door (FD) learners, FD-DR-Learner and FD-R-Learner, under specific assumptions.
result Debiased learners satisfy error bounds and stage-error decompositions, delivering reliable HTE estimates.
New method for valid prediction intervals in counterfactual outcomes with runtime confounding.
problem Valid prediction intervals for counterfactual outcomes under runtime confounding.
method Debiased machine learning framework grounded in semiparametric efficiency theory.
result Prediction intervals achieve desired coverage rates with faster convergence compared to standard methods.
A new method removes biases in data integration by using surrogate control outcomes.
problem Data integration methods can be biased due to data-dependent processes.
method Post-integrated inference method using surrogate control outcomes to account for latent heterogeneity.
result The method provides consistent and efficient estimators under minimal assumptions and potential misspecifications.
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.
Bounds on treatment effect sensitivity in causal reasoning using Hölder's inequality.
problem Estimating treatment effects in presence of unobserved confounders.
method Using Hölder's inequality, derived bounds on confounding bias based on unmeasured confounding strength.
result Bounds are tight under specific conditions of independence between U and T/Y.
Proposes methods to identify and estimate counterfactual distributions with confounding.
problem Estimating counterfactual distributions in the presence of confounding.
method Nonparametric identification and semiparametric estimation using conditional copulas and machine learning.
result Valid inference for individual-level effects and nonparametric identifiability of latent confounding subspace.
New methods identify causal effects without needing complete proxy variables.
problem Identifying causal effects in the presence of unmeasured confounders.
method Partial identification methods that do not require completeness of proxy variables.
result Obtain bounds on causal effects using available proxy variables.