Study clarifies variance of stratification estimators for causal effects.
problem Estimating average causal effects with discrete covariates.
method Combines insights from potential outcomes, causal diagrams, and structural models.
result Derives expressions for the variance of stratification estimators.
Proposes a method to create robust models that adapt to target domains.
problem Creating reliable models when training and target distributions differ.
method Integrates prior knowledge of data generating process to learn stable relationships.
result The Surgery Estimator finds stable relationships in more scenarios than previous methods.
Introduces info intervention to handle causal questions and check counterfactual variables.
problem Controversial interpretation of causal questions for non-manipulable variables and lack of power to check counterfactual variables.
method Intervenes input/output information of causal mechanisms, providing causal diagrams for communication and theoretical focus.
result Causal diagrams based on info intervention provide a new perspective on information transfer as causality.
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.
A new approach to rationalization identifies true rationales by considering causal relationships.
problem Existing rationalization methods struggle with spuriousness, where snippets with similar contributions are hard to distinguish.
method The method leverages causal inference to identify non-spurious rationales, defining probabilities of causation based on a structural causal model.
result The proposed causal rationalization outperforms existing methods on real-world datasets.
The paper develops methods to bound causal effects using Partial Ancestral Graphs.
problem Bounding causal effects from observational data when true causal diagrams are unknown.
method Proposes a method using Partial Ancestral Graphs to derive bounds on causal effects from observational data.
result Demonstrates the effectiveness of the method with synthetic and real data examples.
Generalizes region select game to k-colored knot diagrams.
problem Play game on knot diagrams with multiple colors.
method Generalize region select game to k-colored knot diagrams. result Generalization of the region select game to k-colored knot diagrams. Efficient algorithms decide algebraic constraints of causal graphs.
problem Distinguish causal graphs with latent confounders.
method Study algebraic constraints and propose efficient algorithms.
result Decide equivalence or subset of algebraic constraints.
D2D converts CLDs into SDMs to explore leverage points under uncertainty.
problem Limited dynamic analysis of CLDs for intervention strategies.
method Minimal user input protocol to convert CLDs into SDMs, simulating interventions.
result D2D helps distinguish leverage points and provides uncertainty estimates.
Develops SCMs for latent selection to simplify causal analysis.
problem Latent selection complicates causal analysis.
method Introduces a conditioning operation for SCMs to encode latent selection.
result Conditioning operation preserves simplicity, acyclicity, and linearity of SCMs.
This paper reviews and evaluates causality-based feature selection methods.
problem Capturing causal relationships between features and class variable for better model interpretability.
method Comprehensive review and evaluation of causality-based feature selection algorithms.
result Development of CausalFS package for algorithm comparison and experimentation.
We extend causal calculus to models with cycles, latent confounders, and selection bias.
problem Causal reasoning in the presence of cycles, latent confounders, and selection bias.
method Prove rules of causal calculus for i/o structural causal models, generalize adjustment criteria, and extend ID algorithm.
result Enable causal reasoning in complex models with cycles, latent confounders, and selection bias.
Enhanced framework selects features for unbiased causal inference.
problem Unbiased estimation of causal quantities in causal inference.
method Three-stage computational framework balancing treatment and non-treatment variables.
result Significantly reduces bias and variance in estimating causal quantities.
A new method selects robust features for ML models using causal discovery.
problem Challenges in feature selection for ML models with limited domain knowledge.
method Multidata causal feature selection using PC1 or PCMCI algorithms.
result The method improves model performance and provides interpretable drivers.
The paper discusses selecting predictive models for causal inference, highlighting the challenges and proposing a solution.
problem Selecting the best predictive models for causal inference from a variety of machine learning models.
method The paper proposes using Rext−risk, flexible estimators, and splitting data to compute risks for model selection. result The proposed method controls both outcome errors for treated and non-treated individuals, addressing the issue of model selection for causal inference.
New scoring rule predicts causal relations from data with selection bias.
problem Discovering causal relations from independence constraints under selection bias and confounding.
method Local Y-Structure patterns and a scoring rule for Y-Structures.
result Y-Structure scoring rule successfully predicts causal relations in real-world data.
New algorithm identifies causal relationships from graphs, even with selection bias.
problem Identifying causal relationships from graphs with selection bias.
method Developed a measure-theoretic version of Pearl's causal calculus and a sound, complete identification algorithm.
result General measure-theoretic version of causal calculus allows for identification of causal relationships under selection bias.
Enhanced symplectic quandle colorings detect causal structure in spacetime diagrams.
problem Detecting causal structure in spacetime diagrams using polynomial invariants.
method Comparing symplectic quandle colorings of different diagrams representing spacetime connections.
result Enhanced symplectic quandle colorings consistently distinguish between causally unrelated and related spacetime configurations.
Bayesian model selection improves causal discovery in complex datasets.
problem Identifying causal direction in Markov equivalence classes with realistic assumptions.
method Incorporating causal assumptions within Bayesian framework for model selection.
result Bayesian model selection outperforms previous methods on various datasets.
A new method selects covariates for causal effect estimation without strong assumptions.
problem Estimating causal effects without global causal structure learning and strong assumptions.
method Local covariate selection method that avoids pretreatment and causal sufficiency assumptions.
result The method achieves accurate causal effect estimation with improved computational efficiency.
Paper distinguishes causal structures under latent confounding and selection bias.
problem Distinguishing causal relationships when latent variables and selection bias are present.
method Formulated selected-marginalized directed graphs (smDGs) to distinguish causal structures.
result Two causal structures are indistinguishable if they have the same selected-marginalized directed graph.
New causal measures improve feature selection in AI models.
problem Lack of causal interpretability in AI models.
method Introduces causal entropy and causal information gain to assess feature control.
result Demonstrates superiority of causal information gain in feature selection.
Algorithm bounds causal queries under selection bias.
problem Selection bias affects causal analysis.
method Proposes a new algorithm to address both identifiable and unidentifiable causal queries.
result The likelihood of available data is unimodal, enabling bounds on causal queries.
This work improves task specification learning from demonstrations using maximum causal entropy.
problem Lack of guarantees for safe task composition and historical dependencies in learning from demonstrations.
method Adapting maximum causal entropy inverse reinforcement learning to estimate task specifications using reduced ordered binary decision diagrams.
result Polynomial time algorithm for estimating task specifications from demonstrations.
Bayesian model selection improves multivariate causal discovery without restrictive assumptions.
problem Real-world causal discovery requires flexible assumptions to avoid restrictive model assumptions.
method Continuous relaxation of discrete model selection problem, using Causal Gaussian Process Conditional Density Estimator (CGP-CDE).
result Bayesian approach outperforms traditional methods in multivariate causal discovery.
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.
New causal models perform poorly when evaluated on biased training sets.
problem Sample selection bias affects the evaluation of causal models' prediction performance.
method Re-evaluated prediction performance of causal models on a genetic perturbation data set, proposing a less-biased evaluation set.
result Causal models have similar or worse performance when evaluated on a less-biased set compared to standard association-based estimators.
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias. No constraint-based causal discovery algorithm can currently handle cycles, latent variables and selection bias (CLS) simultaneously. I therefore introduce an algorithm called Cyclic Causal…
Causal predictors don't generalize better across domains than non-causal predictors.
problem How well do causal predictors generalize across different domains?
method 16 prediction tasks on tabular datasets, selecting causal features.
result Causal predictors do not outperform non-causal predictors in domain generalization.
Algorithm recovers causal graphs in presence of latent confounders and selection bias.
problem Recovering causal graphs in the presence of latent confounders and selection bias.
method Iterative causal discovery (ICD) algorithm that relies on causal Markov and faithfulness assumptions.
result Sound and complete algorithm that recovers the equivalence class of the underlying causal graph.
In this paper, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature sel…
The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing data mech…
Improves decision-making by correcting feature selection bias.
problem Cofounding bias in feature selection affects machine learning predictions.
method Proposes a meta-algorithm using a novel adjustment criterion based on causal sufficiency.
result Corrects cofounding bias to improve prediction performance.
Paper introduces v-CMC linking causality and utility.
problem Linking causality and utility for value theory.
method Developed a new causal independence principle (v-CMC) and proved its equivalence.
result Equivalence of local, global, and decomposition versions of v-CMC.
Local learning method selects covariates for causal effect estimation in the presence of latent variables.
problem Estimating causal effects from nonexperimental data with latent variables.
method Local learning approach that identifies valid adjustment sets for causal relationships.
result Ensures soundness and completeness of causal effect estimation under standard assumptions.
FCM clustering adapts to persistence diagrams for topological data analysis.
problem Integrating topological data into machine learning workflows.
method Adapting Fuzzy c-Means to persistence diagrams.
result FCM clustering captures topological structure without additional processing.
New method estimates causal effects in complex spaces using topological structures.
problem Challenges in estimating causal effects in non-Euclidean spaces.
method Developed a topological causal inference framework using power-weighted silhouette functions of persistence diagrams.
result Successfully quantifies topological treatment effects across various complex outcomes.
Expands experimental design for causal discovery from limited data.
problem Challenges in causal discovery from observational and interventional data.
method Bayesian optimal experimental design incorporating recent advances in causal discovery.
result Active causal discovery of large, nonlinear SCMs with both intervention target and value selection.
New method selects direct causal parents from large sets of variables.
problem Inferring direct causal parents from many variables, especially nonlinear and cyclic.
method One-vs.-the-rest feature selection approach with theoretical guarantees.
result Significant improvements over existing methods.
Estimates causal effects with selection bias and confounding using regression.
problem Estimating causal effects in presence of selection bias and confounding.
method Two-step regression estimator (TSR) that corrects for selection bias and accounts for confounding.
result TSR estimator reduces variance and is validated in simulations.
Paper introduces methods to adjust for missing data in causal inference.
problem Missing data and selection bias in causal inference.
method Developed necessary and sufficient conditions for valid adjustment sets.
result Introduced algorithms for finding minimum adjustment sets.
Bayesian method identifies causal sets across populations without graph knowledge.
problem Transporting causal information across populations without causal graph knowledge.
method Combines observational and experimental data to identify s-admissible backdoor sets.
result Proves asymptotic convergence and corrects transportability bias in simulations.
New method selects causal features from diverse data types.
problem Discovering causal relationships from non-continuous data types.
method Transformation-Model (TRAM) based Invariant Causal Prediction (TRAM-ICP) with TRAM-GCM and TRAM-Wald tests.
result Improved power and type I error control for diverse response types.
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.
A method to detect spillover effects and select valid donors for synthetic control models.
problem Identifying valid donors in synthetic control models when spillover effects are possible.
method Theoretical grounding and practical method using pre-intervention data to identify donor values and debias causal estimates.
result A Theorem that identifies assumptions for identifying donor values and debias causal estimates.
New methods estimate heterogeneous causal effects at various levels.
problem Estimating causal effects at different levels of granularity.
method Modified Causal Forests approach for multiple treatment models.
result New estimators outperform existing methods in empirical studies.
New method for selective prediction under interventions learns causal structure from data.
problem Tight uncertainty sets in selective conformal prediction under unknown interventional settings.
method Partial causal structure learning for descendant indicators, contamination-robust coverage theorem, algorithms for descendant discovery and distance estimation.
result Valid selective conformal prediction under contamination up to 30% with controlled coverage.
CausalGame benchmarks LLM agents' causal thinking in games.
problem Evaluating causal thinking in AI Scientists with LLMs.
method Interactive games with 14 scenarios incorporating selection bias, measurement error, and hidden confounders.
result None of the 30 LLM agents demonstrated reliable causal thinking, with the best model achieving only 68.0% survival.