Study relaxes identification assumptions for natural direct effects in non-randomized settings.
problem Identifying causal direct effects under unmeasured confounding.
method Developed relaxed conditions for identifying natural direct effects in non-randomized settings.
result Identified natural direct effect under unmeasured confounding conditions.
New study finds environment significantly suppresses star formation in galaxies, contrary to previous beliefs.
problem Understanding the role of environment in galaxy formation and evolution.
method Applied causal inference framework to IllustrisTNG simulations.
result Environment suppresses star formation by a factor of ~100, contrary to previous beliefs.
CATR rationalizes text data to stabilize causal effect estimation.
problem Observational positivity violation in high-dimensional text data.
method Confounding-Aware Token Rationalization (CATR) selects necessary subset of tokens.
result CATR yields more accurate and stable causal effect estimates.
Optimizes causal effects on unknown graphs using Causal Entropy Optimization.
problem Optimizing causal effects in unknown causal graphs.
method Causal Entropy Optimization (CEO) framework that generalizes Causal Bayesian Optimization (CBO). Incorporates causal structure uncertainty in surrogate models and intervention selection.
result CEO achieves faster convergence to global optimum compared to CBO and improves upon sequential structure learning.
Extends causal inference to hidden mediators with proxies.
problem Identifying causal effects with hidden mediators and error-prone proxies.
method Established causal hidden mediation analysis and hidden front-door criterion.
result Identification of population intervention indirect effect possible with hidden mediators.
DeepMed uses DNNs to estimate causal mediation effects without sparsity constraints.
problem Estimating Natural Direct and Indirect Effects in mediation analysis.
method DeepMed employs deep neural networks to cross-fit infinite-dimensional nuisance functions.
result DeepMed achieves semiparametric efficiency bound and adapts to low-dimensional nuisance structures.
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.
Proposes a new Alzheimer's disease simulator for causal effect estimation.
problem Lack of suitable benchmarks for evaluating causal effect estimators in real-world healthcare data.
method Developed a simulator of Alzheimer's disease using ADNI dataset, incorporating various parameters to model complexities.
result Compared estimators of average and conditional treatment effects using the new simulator.
Paper proposes learning causal graphs with only relevant variables.
problem Discovering causal relationships in large-scale graphs often includes irrelevant variables.
method Developed NSCSL algorithm to learn necessary and sufficient causal graphs (NSCG).
result NSCSL algorithm identifies relevant causal features for specific outcomes.
Causal Interaction Trees identify treatment subgroup effects in observational data.
problem Identifying subgroups with enhanced treatment effects in observational studies.
method Extending Classification and Regression Trees with subgroup-specific treatment effect estimators.
result The proposed algorithms enhance treatment effect heterogeneity in subgroups.
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.
Quantum theory challenges traditional cause-effect relations, showing causal influences even without Bell inequality violations.
problem Challenging traditional concepts of cause-effect relations in quantum mechanics.
method Introducing a general framework to estimate causal influences without interventions or classical/quantum assumptions.
result Every pure bipartite entangled state violates classical bounds on causal influence, negating the idea that Bell inequalities are the only signature of incompatibility.
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.
Autoregressive flow models can perform causal discovery and inference tasks.
problem Causal inference tasks such as causal discovery and interventional predictions.
method Using autoregressive flow models to estimate causal directions and make predictions.
result Autoregressive flows can accurately perform causal inference tasks without restrictive assumptions.
New method estimates bidirectional causal effects in large-scale systems.
problem Estimating bidirectional causal effects in systems with mutual dependence and heteroskedasticity.
method Heteroskedasticity-based identification with online kernel learning and random Fourier features.
result Superior accuracy and stability compared to single equation and polynomial approximations.
Study efficient inference for network quantile causal effects with partial interference.
problem Estimating network causal effects on outcome quantiles with partial interference.
method Developed a nonparametric efficiency theory and a nonparametrically efficient estimator using a three-way cross-fitting procedure.
result Proposed estimator is consistent, asymptotically normal, and allows flexible estimation of nuisance functions.
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.
We define transit clusters to simplify causal diagrams and preserve their essential properties.
problem Clustering variables in causal diagrams can alter essential properties of causal effects.
method We define transit clusters and provide an algorithm to find them, ensuring they preserve causal effect identifiability.
result Transit clusters simplify causal effect identification and maintain their essential properties.
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.
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.
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.
New method uses few instruments to estimate complex causal effects.
problem Estimating causal effects with limited instruments in high-dimensional settings.
method Sequentially selects and combines instruments to estimate the treatment effect.
result Can reliably recover the treatment effect's projection onto the instrumented subspace.
Aggregated variables can mask causal effects, turning unconfounded into confounded relations.
problem Aggregated variables can mask causal effects, leading to paradoxical confounding.
method Analysis of how aggregated variables can change the definition of causality and the feasibility of causal relations.
result Macro causal relations are defined by micro states, not just aggregated variables.
Develops variable-lag Granger causality for more accurate time series analysis.
problem Fixed time delay assumption in Granger causality does not fit many real-world applications.
method Variable-lag Granger causality, inferring with arbitrary time delays.
result Performs better than existing methods in coordinated collective behavior studies.
Bayesian method estimates causal effects with proxy networks.
problem Estimating causal effects with only proxy measurements of a latent interference network.
method Structural causal model with Block Gibbs sampler and Locally Informed Proposals.
result Accurately estimates causal effects even with noisy proxy networks.
Paper introduces ps-BART for estimating nonlinear ATE and CATE in continuous treatments.
problem Estimating ATE and CATE in continuous treatments with nonlinear relationships.
method Generalized ps-BART model for nonparametric estimation.
result ps-BART outperforms BCF model in highly nonlinear settings.
Discovering causal relations is fundamental to reasoning and intelligence. In particular, observational causal discovery algorithms estimate the cause-effect relation between two random entities X and Y, given n samples from P(X,Y). In this paper, we develop a framework to estimate the cause-effect relation bet…
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.
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.
A new framework estimates causal effects for ordinal variables.
problem Existing causal inference methods fail for ordinal data.
method Presumes a latent Gaussian DAG model with constrained covariance matrix.
result Closed-form function for ordinal causal effects in latent space.
Theory and methods to mitigate omitted variable bias in causal machine learning.
problem Mitigating omitted variable bias in causal machine learning models.
method Developed a general theory and flexible statistical inference methods for bounding and testing the magnitude of omitted variable bias.
result Simple plausibility judgments can bound the magnitude of omitted variable bias in complex, nonlinear models.
Generative framework improves causal estimation from observational data.
problem Estimating individualized treatment effects from non-randomized data.
method Importance-Weighted Diffusion Distillation (IWDD) combining diffusion models and IPW.
result IWDD achieves state-of-the-art prediction performance and significantly improves causal estimation.
New framework removes harmful momentum effect for long-tailed classification.
problem Challenges in maintaining balanced datasets with long-tailed data.
method Causal inference framework to disentangle and remove harmful effects of momentum.
result Achieves state-of-the-art performance on long-tailed visual recognition benchmarks.
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.
Develops tools to decompose spurious variations in causal models.
problem Understanding and decomposing spurious variations in causal relationships.
method Formal tools for decomposing spurious effects in Markovian and Semi-Markovian models.
result First results on non-parametric decomposition of spurious effects and sufficient conditions for identification.
Aggregation challenges causal interpretation of IV estimators.
problem Aggregation of fine-grained components into an aggregate treatment variable.
method Characterization of conditions for identifying aggregate causal effects.
result Standard IV estimators cannot identify aggregate causal effects due to ambiguous dependencies.
This paper frames causal structure estimation as a machine learning task. The idea is to treat indicators of causal relationships between variables as `labels' and to exploit available data on the variables of interest to provide features for the labelling task. Background scientific knowledge or any available interven…
Causal Bayesian networks interpret actions as interventions to connect models to real-world outcomes.
problem Connecting causal model predictions to real-world outcomes.
method Formal framework to interpret actions as interventions and prove impossibility results.
result No non-circular interpretation exists that satisfies natural desiderata without violating some.
The paper tackles stock prediction models by improving their generalizability to out-of-sample domains using causal representation learning.
problem Low signal-to-noise ratio and nonstationary nature of financial markets lead to poor performance of stock prediction models.
method The paper investigates Domain Generalization techniques, focusing on causal representation learning to improve model generalizability. It introduces a novel error bound and a causal discovery technique to mitigate spurious correlations.
result The proposed approach enhances the generalizability of stock prediction models, as demonstrated by numerical results.
Method infers TCR effects on disease using patient data.
problem Estimating causal effects of TCR sequences on patient outcomes.
method Corrects for confounders using pre-selection TCR data and neural-network estimator.
result Identifies potentially therapeutic TCRs for COVID-19 severity.
Develops MgCSL for discovering causal structures in high-dimensional data.
problem Discovering causal relationships from high-dimensional data with complex interplay of variables.
method MgCSL uses sparse auto-encoders for coarse-graining and multi-layer perceptrons for detailed analysis, introducing simplified acyclicity constraints.
result MgCSL outperforms existing methods and finds explainable causal connections in fMRI datasets.
SLEM uses machine learning to improve causal inference from observational data.
problem Improving causal inference from observational data using non-linear relationships.
method Super Learner Equation Modeling integrating machine learning ensembles.
result SLEM provides consistent and unbiased estimates of causal effects.
Meta-learning model predicts intervention effects from uncertain causal graphs.
problem Estimating intervention effects when causal structures are uncertain.
method Model-Averaged Causal Estimation Transformer Neural Process (MACE-TNP) using meta-learning.
result MACE-TNP outperforms Bayesian baselines in predicting intervention distributions.
Rhino learns causal relationships from time series data with history-dependent noise.
problem Discovering causal relationships from time series data with non-linear relations, instantaneous effects, and history-dependent noise.
method Combines vector auto-regression, deep learning, and variational inference.
result Demonstrates better causal relationship discovery performance compared to baselines.
iCITRIS learns causal variables from interactive systems with instantaneous effects.
problem Identifying causal variables from temporal sequences with instantaneous effects.
method iCITRIS method for causal representation learning that handles instantaneous effects in intervened temporal sequences.
result iCITRIS accurately identifies causal variables and their causal graph from three interactive system datasets.
Predictions can shape outcomes, study helps predict these effects.
problem Understanding how predictions influence real-world outcomes.
method Causal identifiability analysis of prediction-covariate-outcome relationships.
result Standard supervised learning can identify transferable relationships from predictions.
Develops variable-lag Granger causality and Transfer Entropy for time series analysis.
problem Fixed time delay assumption in Granger causality and Transfer Entropy does not hold in many applications.
method Variable-lag Granger causality and Transfer Entropy, using optimal warping path of Dynamic Time Warping (DTW).
result Proposed methods perform better than existing methods in both simulated and real-world datasets.
Identifies causal effects in LiNGAM models with latent variables.
problem Identifying causal effects in LiNGAM models with latent confounders.
method Complete graphical characterization and efficient algorithms for certification. RICA adaptation for estimation.
result Efficient algorithms and RICA adaptation for estimating causal effects.