New pipeline for causal research in psychology and social sciences.
problem Underuse of causal approaches in psychology and social science.
method Formal specification of theories, reduction of complexity, estimation of causal effects.
result Facilitates scientific inquiry compatible with testing causal theories.
Research aims to bridge statistical learning to causal models in AI.
problem Challenges in machine learning and AI related to causality.
method Transition from statistical learning to causal models.
result Progress in AI may require advances in causal modeling.
SCARY dataset generates complex causal scenarios for causality research.
problem Lack of complexity in existing causal datasets.
method Synthetic dataset with 40 scenarios, three seeds, and two data generation mechanisms.
result Provides a valuable resource for realistic causal discovery.
This survey explores causal inference in banking, finance, and insurance.
problem Explaining decisions in banking, finance, and insurance using causal inference.
method Categorizes 37 papers on causal inference applications in banking, finance, and insurance.
result Causal inference is still in its infancy in banking and insurance sectors.
CausalBench aims to advance causal learning research with a transparent platform.
problem Lack of unified benchmark datasets, algorithms, metrics, and evaluation interfaces for causal learning.
method Introduces CausalBench, a flexible benchmark framework for causal analysis and machine learning.
result Promotes scientific collaboration, reproducibility, and awareness in causal learning research.
Survey on causal inference methods for observational data.
problem Estimating causal effects from observational data.
method Comprehensive review of causal inference methods under the potential outcome framework.
result Various causal effect estimation methods have been developed and compared.
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…
Crowdsourced wisdom improves causal learning.
problem Improving causal learning through collective intelligence.
method Crowdsourcing, expert knowledge elicitation, aggregation techniques, and LLMs.
result Collective contributions enhance global causal structure.
This tutorial introduces causal modeling methods for researchers.
problem Understanding causal relationships in research studies.
method Integrates potential outcomes and graphical methods for causal modeling.
result Clear notation and practical examples for applied researchers.
CDM and CEE differ; accurate CEE isn't needed for CDM.
problem Understanding the difference between CDM and CEE.
method Highlighting the distinctions and implications of CDM and CEE.
result Accurate CEE is not necessary for accurate CDM.
Double machine learning improves causal effect estimation by relaxing assumptions.
problem Estimating causal effects with observational data.
method Double/debiased machine learning (DML) framework.
result DML improves adjustment for nonlinear confounding relationships.
We develop a method to learn abstract causal graphs from interventional data.
problem Estimating causal models at fine granularity is impractical or undesirable.
method Novel graphical identifiability results and an efficient algorithm.
result Directly learns abstract causal graphs from interventional data.
Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable inference as well as complicate the choices that an applied researcher ha…
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.
This paper explores causal analysis in machine learning for better interpretability.
problem The lack of causality in traditional interpretable machine learning models.
method An overview of causal approaches for interpretable machine learning.
result Causal analysis improves the interpretability of machine learning models.
Commentary on Cheng's fairness comparison between tests and AI.
problem Distinction between equality and equity in fairness.
method Systematic comparison of test fairness and algorithmic fairness.
result Importance of causality in fairness research.
Proposes efficient bounds for causal effect estimation under weak confounding.
problem Estimating causal effects with weakly confounded variables.
method Develops an efficient linear program to derive upper and lower bounds on causal effect under small entropy of unobserved confounders.
result Bounds are consistent and tighter for weakly confounded variables.
New method for learning causal relationships in PNL models.
problem Learning causal relationships from empirical observations in PNL models.
method Rank-based methods to estimate non-linear functions, disentangling from independence tests.
result Consistent method for PNL causal discovery, validated in experiments.
Paper introduces active and passive causal inference techniques.
problem Causal inference in machine learning.
method Categorizes causal inference techniques into active and passive approaches.
result Describes and discusses various causal inference methods.
Extracts causal brain dynamics across multiple scales.
problem Statistical associations do not reflect causal mechanisms in brain dynamics.
method Multiscale causal backbone (MCB) extraction using advanced causal structure learning.
result Sparse MCBs reveal distinct causal roles at different brain frequency bands.
Proposes K-Fold Causal BART for improved CATE estimation.
problem Improving estimation of Conditional Average Treatment Effects (CATE).
method K-Fold Causal Bayesian Additive Regression Trees (K-Fold Causal BART).
result K-Fold Causal BART is not state-of-the-art for ATE and CATE estimation in the IHDP dataset, but provides insights into model robustness and evaluation methods.
Scoping review of EO-ML methods for causal inference in poverty geography.
problem Lack of thorough documentation and best practices for EO-ML methods in causal analysis.
method Comprehensive scoping review cataloging five principal approaches.
result Detailed protocol for integrating EO data into causal analysis.
Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowle…
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.
New algorithms for causal bandits without knowing the graph structure.
problem Causal bandit problems with unknown graph structure.
method Developed novel causal bandit algorithms for causal trees, forests, and general graphs without prior knowledge of the causal graph.
result Regret guarantees significantly improved over standard MAB algorithms under mild conditions.
Probabilistic models can handle causal inference without special tools.
problem Confusion over necessary tools for causal inference.
method Demonstrated through concrete examples that causal questions can be answered using standard probabilistic models.
result Causal questions can be addressed using standard probabilistic modelling and inference.
Python library for causal discovery from observational data.
problem Revealing causal relations from observational data.
method Comprehensive collection of causal discovery methods in Python.
result Ease of use for non-specialists and modular building blocks for developers.
GRACE-C improves causal learning from time series data.
problem Misleading causal information due to mismatched timescales.
method Combines constraint programming with theoretical insights and prior information.
result Significantly faster and scalable causal learning for large datasets.
The paper reviews MARL and its causal challenges, advocating for a 'causality first' approach.
problem Challenges in multi-agent reinforcement learning (MARL) and lack of theoretical guarantees for emergent behavior.
method Discussion of how causal methods can improve safety, interpretability, and robustness in MARL.
result Causal methods can provide strong theoretical guarantees for emergent behavior in MARL.
Proposes a framework for causal inference with processed outcomes in biomedical research.
problem Impact of intra-subject processing on inter-subject statistical inference in biomedical research.
method Semiparametric framework with multiply robust estimators and step-down procedure for high-dimensional inference.
result Superior performance of the proposed approach demonstrated through simulations and application to autism research.
Framework identifies causal factors of climate change using correlations and machine learning.
problem Understanding socioeconomic factors influencing carbon emissions and climate change.
method Three-step framework: correlation analysis, causal discovery, LLM interpretations.
result Adaptable solutions for data-driven policy-making and strategic decision-making.
New measures for causal entropy and information gain studied.
problem Quantifying causal relationships in machine learning.
method Formal study of causal entropy and information gain.
result Established fundamental properties and relationships.
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.
Automated method bounds causal effects in discrete data.
problem Partial identification of causal effects in discrete settings.
method Polynomial programming and dual relaxation for automated bounds.
result Algorithm provides guaranteed non-sharp and ε-sharp bounds. 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.
New technique learns causally disentangled representations for better generation.
problem Learning disentangled representations for accurate generation.
method Causally Disentangled Generation (CDG) approach with supervised regularization.
result CDG is necessary and sufficient for accurate disentangled generation.
Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard …
Discovering causal relationships from data is the ultimate goal of many research areas. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and have many applications. A common problem with these methods is the high computational complex…
Novel causal effect estimators and distributionally robust prediction methods.
problem Estimating causal effects and distributional robustness in statistical models.
method Developed novel estimators and proposed a general framework for distributional robustness.
result Mean squared error improvements in causal effect estimation compared to existing methods.
This review explores causal decision-making to improve decision quality.
problem Effective decision-making requires understanding causal relationships.
method Causal structure learning, causal effect learning, and causal policy learning.
result Challenges in causal decision-making are identified and recent advances are discussed.
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.
Investment strategy developed using causal discovery algorithms in equity markets.
problem Lack of actionable causal relationships in large equity markets.
method Causal discovery algorithms applied to equity market data.
result Causal discovery algorithms can uncover actionable causal relationships in equity markets, leading to profitable investment outcomes.
Proposes a new framework to evaluate causal discovery methods for time series data.
problem Lack of ground truth for causal discovery in time series data.
method Flexible framework for generating synthetic time series data.
result Demonstrates degradation in performance when assumptions are violated.
Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers. This paper develops new estimation and inference procedures for multiple treatment models in a selection-on-observables framework by modifying the Causal Fore…
Causal discovery improves fMRI analysis, but faces challenges.
problem Challenges in applying causal discovery to fMRI data.
method Identifying and addressing nine challenges in fMRI causal discovery.
result Current methods for fMRI causal discovery need improvement.
A new method for estimating causal effects using synthetic controls.
problem Evaluating causal effects of policy changes in settings with observational data.
method Distributional Synthetic Controls method.
result Allows construction of entire synthetic distributions for the treated unit.
This work frames reward modelling from preferences as a causal problem.
problem Reward modelling from preference data for AI alignment.
method Causal inference approach to identify challenges and assumptions.
result Causally-inspired approaches improve model robustness.
Transformer model handles causal inference with DAG integration.
problem Complex causal structures and adaptability across various scenarios.
method Integrates DAGs into transformer's attention mechanism.
result Surpasses existing methods in estimating causal effects.