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Tag: causal

  • Scalable Contrastive Causal Discovery under Unknown Soft Interventions

    Scalable Contrastive Causal Discovery under Unknown Soft Interventions arXiv:2603.03411v1 Announce Type: new Abstract: Observational causal discovery is only identifiable up to the Markov equivalence class. While interventions can reduce this ambiguity, in practice interventions are often soft with multiple unknown targets. In many realistic scenarios, only a single intervention regime is observed. We propose a…

  • Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables

    Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables arXiv:2602.23611v1 Announce Type: new Abstract: Algorithmic decisions about individuals require predictions that are not only accurate but also fair with respect to sensitive attributes such as gender and race. Causal notions of fairness align with legal requirements, yet many…

  • Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data

    Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data arXiv:2602.23602v1 Announce Type: new Abstract: Heteroscedasticity — where the variance of a variable changes with other variables — is pervasive in real data, and elucidating why it arises from the perspective of statistical moments is crucial in scientific knowledge discovery and decision-making. However,…

  • Sparse Additive Model Pruning for Order-Based Causal Structure Learning

    Sparse Additive Model Pruning for Order-Based Causal Structure Learning arXiv:2602.15306v1 Announce Type: new Abstract: Causal structure learning, also known as causal discovery, aims to estimate causal relationships between variables as a form of a causal directed acyclic graph (DAG) from observational data. One of the major frameworks is the order-based approach that first estimates a…

  • Causal Effect Estimation with Learned Instrument Representations

    Causal Effect Estimation with Learned Instrument Representations arXiv:2602.10370v1 Announce Type: new Abstract: Instrumental variable (IV) methods mitigate bias from unobserved confounding in observational causal inference but rely on the availability of a valid instrument, which can often be difficult or infeasible to identify in practice. In this paper, we propose a representation learning approach that…

  • Efficient Causal Structure Learning via Modular Subgraph Integration

    Efficient Causal Structure Learning via Modular Subgraph Integration arXiv:2601.21014v1 Announce Type: new Abstract: Learning causal structures from observational data remains a fundamental yet computationally intensive task, particularly in high-dimensional settings where existing methods face challenges such as the super-exponential growth of the search space and increasing computational demands. To address this, we introduce VISTA (Voting-based…

  • Data-Driven Information-Theoretic Causal Bounds under Unmeasured Confounding

    Data-Driven Information-Theoretic Causal Bounds under Unmeasured Confounding arXiv:2601.17160v1 Announce Type: new Abstract: We develop a data-driven information-theoretic framework for sharp partial identification of causal effects under unmeasured confounding. Existing approaches often rely on restrictive assumptions, such as bounded or discrete outcomes; require external inputs (for example, instrumental variables, proxies, or user-specified sensitivity parameters); necessitate full…

  • Causal ML for the Aspiring Data Scientist

    Causal ML for the Aspiring Data Scientist An accessible introduction to causal inference and ML The post Causal ML for the Aspiring Data Scientist appeared first on Towards Data Science. Ross Lauterbach Go to original source

  • Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach

    Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach arXiv:2601.11016v1 Announce Type: new Abstract: In this paper, we introduce a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the underlying distribution by developing interpretable and tractable decision rules that prescribe decisions using covariates.…

  • Coarsening Causal DAG Models

    Coarsening Causal DAG Models arXiv:2601.10531v1 Announce Type: new Abstract: Directed acyclic graphical (DAG) models are a powerful tool for representing causal relationships among jointly distributed random variables, especially concerning data from across different experimental settings. However, it is not always practical or desirable to estimate a causal model at the granularity of given features in…

  • Identification and Estimation under Multiple Versions of Treatment: Mixture-of-Experts Approach

    Identification and Estimation under Multiple Versions of Treatment: Mixture-of-Experts Approach arXiv:2601.00287v1 Announce Type: cross Abstract: The Stable Unit Treatment Value Assumption (SUTVA) includes the condition that there are no multiple versions of treatment in causal inference. Though we could not control the implementation of treatment in observational studies, multiple versions may exist in the treatment.…

  • Robust Causal Directionality Inference in Quantum Inference under MNAR Observation and High-Dimensional Noise

    Robust Causal Directionality Inference in Quantum Inference under MNAR Observation and High-Dimensional Noise arXiv:2512.19746v1 Announce Type: new Abstract: In quantum mechanics, observation actively shapes the system, paralleling the statistical notion of Missing Not At Random (MNAR). This study introduces a unified framework for textbf{robust causal directionality inference} in quantum engineering, determining whether relations are system$to$observation,…

  • Learning Causality for Longitudinal Data

    Learning Causality for Longitudinal Data arXiv:2512.04980v1 Announce Type: new Abstract: This thesis develops methods for causal inference and causal representation learning (CRL) in high-dimensional, time-varying data. The first contribution introduces the Causal Dynamic Variational Autoencoder (CDVAE), a model for estimating Individual Treatment Effects (ITEs) by capturing unobserved heterogeneity in treatment response driven by latent risk…

  • Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning

    Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning arXiv:2511.05050v1 Announce Type: new Abstract: In this study, a scalable online kernel learning framework is proposed for estimating bidirectional causal effects in systems characterized by mutual dependence and heteroskedasticity. Traditional causal inference often focuses on unidirectional effects, overlooking the common bidirectional relationships in real-world phenomena.…

  • DoFlow: Causal Generative Flows for Interventional and Counterfactual Time-Series Prediction

    DoFlow: Causal Generative Flows for Interventional and Counterfactual Time-Series Prediction arXiv:2511.02137v1 Announce Type: new Abstract: Time-series forecasting increasingly demands not only accurate observational predictions but also causal forecasting under interventional and counterfactual queries in multivariate systems. We present DoFlow, a flow based generative model defined over a causal DAG that delivers coherent observational and interventional…

  • Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference

    Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference arXiv:2510.21889v1 Announce Type: new Abstract: Causal inference identifies cause-and-effect relationships between variables. While traditional approaches rely on data to reveal causal links, a recently developed method, assimilative causal inference (ACI), integrates observations with dynamical models. It utilizes Bayesian data assimilation…

  • Coupling Generative Modeling and an Autoencoder with the Causal Bridge

    Coupling Generative Modeling and an Autoencoder with the Causal Bridge arXiv:2509.25599v1 Announce Type: new Abstract: We consider inferring the causal effect of a treatment (intervention) on an outcome of interest in situations where there is potentially an unobserved confounder influencing both the treatment and the outcome. This is achievable by assuming access to two separate…

  • What is a good matching of probability measures? A counterfactual lens on transport maps

    What is a good matching of probability measures? A counterfactual lens on transport maps arXiv:2509.16027v1 Announce Type: new Abstract: Coupling probability measures lies at the core of many problems in statistics and machine learning, from domain adaptation to transfer learning and causal inference. Yet, even when restricted to deterministic transports, such couplings are not identifiable:…

  • Analysis of Sales Shift in Retail with Causal Impact: A Case Study at Carrefour

    Analysis of Sales Shift in Retail with Causal Impact: A Case Study at Carrefour Applying causal inference to measure the effect of product unavailability on retail sales at Carrefour The post Analysis of Sales Shift in Retail with Causal Impact: A Case Study at Carrefour appeared first on Towards Data Science. Thanh Liêm NGUYEN Go…

  • Causal-Symbolic Meta-Learning (CSML): Inducing Causal World Models for Few-Shot Generalization

    Causal-Symbolic Meta-Learning (CSML): Inducing Causal World Models for Few-Shot Generalization arXiv:2509.12387v1 Announce Type: cross Abstract: Modern deep learning models excel at pattern recognition but remain fundamentally limited by their reliance on spurious correlations, leading to poor generalization and a demand for massive datasets. We argue that a key ingredient for human-like intelligence-robust, sample-efficient learning-stems from…

  • BaMANI: Bayesian Multi-Algorithm causal Network Inference

    BaMANI: Bayesian Multi-Algorithm causal Network Inference arXiv:2508.11741v1 Announce Type: new Abstract: Improved computational power has enabled different disciplines to predict causal relationships among modeled variables using Bayesian network inference. While many alternative algorithms have been proposed to improve the efficiency and reliability of network prediction, the predicted causal networks reflect the generative process but also…

  • Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: “One Map, Many Trials” in Satellite-Driven Poverty Analysis

    Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: “One Map, Many Trials” in Satellite-Driven Poverty Analysis arXiv:2508.01341v1 Announce Type: new Abstract: Machine learning models trained on Earth observation data, such as satellite imagery, have demonstrated significant promise in predicting household-level wealth indices, enabling the creation of high-resolution wealth maps that can…

  • Probably Approximately Correct Causal Discovery

    Probably Approximately Correct Causal Discovery arXiv:2507.18903v1 Announce Type: new Abstract: The discovery of causal relationships is a foundational problem in artificial intelligence, statistics, epidemiology, economics, and beyond. While elegant theories exist for accurate causal discovery given infinite data, real-world applications are inherently resource-constrained. Effective methods for inferring causal relationships from observational data must perform well…

  • LILI clustering algorithm: Limit Inferior Leaf Interval Integrated into Causal Forest for Causal Interference

    LILI clustering algorithm: Limit Inferior Leaf Interval Integrated into Causal Forest for Causal Interference arXiv:2507.03271v1 Announce Type: new Abstract: Causal forest methods are powerful tools in causal inference. Similar to traditional random forest in machine learning, causal forest independently considers each causal tree. However, this independence consideration increases the likelihood that classification errors in one…

  • Prescriptive Modeling Makes Causal Bets – Whether You Know it or Not!

    Prescriptive Modeling Makes Causal Bets – Whether You Know it or Not! An explanation of the causal assumption implicit in prescriptive modeling and how to satisfy it. The post Prescriptive Modeling Makes Causal Bets – Whether You Know it or Not! appeared first on Towards Data Science. Jarom Hulet Go to original source

  • Double Machine Learning for Conditional Moment Restrictions: IV regression, Proximal Causal Learning and Beyond

    Double Machine Learning for Conditional Moment Restrictions: IV regression, Proximal Causal Learning and Beyond arXiv:2506.14950v1 Announce Type: new Abstract: Solving conditional moment restrictions (CMRs) is a key problem considered in statistics, causal inference, and econometrics, where the aim is to solve for a function of interest that satisfies some conditional moment equalities. Specifically, many techniques…

  • A Practical Starters’ Guide to Causal Structure Learning with Bayesian Methods in Python

    A Practical Starters’ Guide to Causal Structure Learning with Bayesian Methods in Python Learn Causal Structures and make inferences with Bayesian Methods: Python Tutorial The post A Practical Starters’ Guide to Causal Structure Learning with Bayesian Methods in Python appeared first on Towards Data Science. Erdogan Taskesen Go to original source

  • Nonlinear Causal Discovery through a Sequential Edge Orientation Approach

    Nonlinear Causal Discovery through a Sequential Edge Orientation Approach arXiv:2506.05590v1 Announce Type: new Abstract: Recent advances have established the identifiability of a directed acyclic graph (DAG) under additive noise models (ANMs), spurring the development of various causal discovery methods. However, most existing methods make restrictive model assumptions, rely heavily on general independence tests, or require…

  • Nonlinear Causal Discovery for Grouped Data

    Nonlinear Causal Discovery for Grouped Data arXiv:2506.05120v1 Announce Type: new Abstract: Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many important domains, including neuroscience, psychology, social science, and industrial manufacturing, the causal units of interest are groups of variables rather…

  • Identifiability of latent causal graphical models without pure children

    Identifiability of latent causal graphical models without pure children arXiv:2505.18410v1 Announce Type: new Abstract: This paper considers a challenging problem of identifying a causal graphical model under the presence of latent variables. While various identifiability conditions have been proposed in the literature, they often require multiple pure children per latent variable or restrictions on the…

  • On Measuring Intrinsic Causal Attributions in Deep Neural Networks

    On Measuring Intrinsic Causal Attributions in Deep Neural Networks arXiv:2505.09660v1 Announce Type: new Abstract: Quantifying the causal influence of input features within neural networks has become a topic of increasing interest. Existing approaches typically assess direct, indirect, and total causal effects. This work treats NNs as structural causal models (SCMs) and extends our focus to…

  • Regression Discontinuity Design: How It Works and When to Use It

    Regression Discontinuity Design: How It Works and When to Use It Regression Discontinuity Design: How It Works and When to Use It You’re an avid data scientist and experimenter. You know that randomisation is the summit of Mount Evidence Credibility, and you also know that when you can’t randomise, you resort to observational data and…

  • Characterization and Learning of Causal Graphs from Hard Interventions

    Characterization and Learning of Causal Graphs from Hard Interventions arXiv:2505.01037v1 Announce Type: new Abstract: A fundamental challenge in the empirical sciences involves uncovering causal structure through observation and experimentation. Causal discovery entails linking the conditional independence (CI) invariances in observational data to their corresponding graphical constraints via d-separation. In this paper, we consider a general…

  • Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows

    Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows arXiv:2504.04013v1 Announce Type: new Abstract: Post-earthquake hazard and impact estimation are critical for effective disaster response, yet current approaches face significant limitations. Traditional models employ fixed parameters regardless of geographical context, misrepresenting how seismic effects vary across diverse…

  • Causal Bayesian Optimization with Unknown Graphs

    Causal Bayesian Optimization with Unknown Graphs arXiv:2503.19554v1 Announce Type: new Abstract: Causal Bayesian Optimization (CBO) is a methodology designed to optimize an outcome variable by leveraging known causal relationships through targeted interventions. Traditional CBO methods require a fully and accurately specified causal graph, which is a limitation in many real-world scenarios where such graphs are…

  • Interpretable Neural Causal Models with TRAM-DAGs

    Interpretable Neural Causal Models with TRAM-DAGs arXiv:2503.16206v1 Announce Type: new Abstract: The ultimate goal of most scientific studies is to understand the underlying causal mechanism between the involved variables. Structural causal models (SCMs) are widely used to represent such causal mechanisms. Given an SCM, causal queries on all three levels of Pearl’s causal hierarchy can…

  • On the Identifiability of Causal Abstractions

    On the Identifiability of Causal Abstractions arXiv:2503.10834v1 Announce Type: new Abstract: Causal representation learning (CRL) enhances machine learning models’ robustness and generalizability by learning structural causal models associated with data-generating processes. We focus on a family of CRL methods that uses contrastive data pairs in the observable space, generated before and after a random, unknown…

  • Addressing pitfalls in implicit unobserved confounding synthesis using explicit block hierarchical ancestral sampling

    Addressing pitfalls in implicit unobserved confounding synthesis using explicit block hierarchical ancestral sampling arXiv:2503.09194v1 Announce Type: new Abstract: Unbiased data synthesis is crucial for evaluating causal discovery algorithms in the presence of unobserved confounding, given the scarcity of real-world datasets. A common approach, implicit parameterization, encodes unobserved confounding by modifying the off-diagonal entries of the…

  • Learning Causal Response Representations through Direct Effect Analysis

    Learning Causal Response Representations through Direct Effect Analysis arXiv:2503.04358v1 Announce Type: new Abstract: We propose a novel approach for learning causal response representations. Our method aims to extract directions in which a multidimensional outcome is most directly caused by a treatment variable. By bridging conditional independence testing with causal representation learning, we formulate an optimisation…

  • A Review of Causal Decision Making

    A Review of Causal Decision Making arXiv:2502.16156v1 Announce Type: new Abstract: To make effective decisions, it is important to have a thorough understanding of the causal relationships among actions, environments, and outcomes. This review aims to surface three crucial aspects of decision-making through a causal lens: 1) the discovery of causal relationships through causal structure…

  • Batch-Adaptive Annotations for Causal Inference with Complex-Embedded Outcomes

    Batch-Adaptive Annotations for Causal Inference with Complex-Embedded Outcomes arXiv:2502.10605v1 Announce Type: new Abstract: Estimating the causal effects of an intervention on outcomes is crucial. But often in domains such as healthcare and social services, this critical information about outcomes is documented by unstructured text, e.g. clinical notes in healthcare or case notes in social services.…

  • SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph

    SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph arXiv:2502.07857v1 Announce Type: new Abstract: Causal discovery can be computationally demanding for large numbers of variables. If we only wish to estimate the causal effects on a small subset of target variables, we might not need to learn the causal graph for…

  • Causal vs. Anticausal merging of predictors

    Causal vs. Anticausal merging of predictors arXiv:2501.08426v1 Announce Type: cross Abstract: We study the differences arising from merging predictors in the causal and anticausal directions using the same data. In particular we study the asymmetries that arise in a simple model where we merge the predictors using one binary variable as target and two continuous…

  • Dynamic Causal Structure Discovery and Causal Effect Estimation

    Dynamic Causal Structure Discovery and Causal Effect Estimation arXiv:2501.06534v1 Announce Type: new Abstract: To represent the causal relationships between variables, a directed acyclic graph (DAG) is widely utilized in many areas, such as social sciences, epidemics, and genetics. Many causal structure learning approaches are developed to learn the hidden causal structure utilizing deep-learning approaches. However,…

  • Contrastive representations of high-dimensional, structured treatments

    Contrastive representations of high-dimensional, structured treatments arXiv:2411.19245v1 Announce Type: new Abstract: Estimating causal effects is vital for decision making. In standard causal effect estimation, treatments are usually binary- or continuous-valued. However, in many important real-world settings, treatments can be structured, high-dimensional objects, such as text, video, or audio. This provides a challenge to traditional causal…