The concept of causality has a controversial history. The question of whether it is possible to represent and address causal problems with probability theory, or if fundamentally new mathematics such as the do calculus is required has been hotly debated, e.g. Pearl (2001) states "the building blocks of our scientific a…
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30 results for “do-calculus”
Paper characterizes causal graphs from hard interventions and proposes a learning algorithm.
problem Discovering causal structure from hard interventions and observational data.
method Proposes graphical constraints and a learning algorithm based on do-calculus.
result Characterizes interventional equivalence classes of causal graphs with latent variables.
We establish causal semantics for SDEs and develop methods to reason about them.
problem Understanding causal relationships in systems modeled by stochastic differential equations.
method We introduce a causal graph framework, Markov properties, and do-calculus for SDEs.
result We prove the -separation Markov property and do-calculus for causal SDEs.
The concept of causality has a controversial history. The question of whether it is possible to represent and address causal problems with probability theory, or if fundamentally new mathematics such as the do-calculus is required has been hotly debated, In this paper we demonstrate that, while it is critical to explic…
The paper compares and optimizes estimators for treatment effects with observed confounders and mediators.
problem Estimating treatment effects with observed confounders and mediators.
method Investigates the linear Gaussian causal model, compares and optimizes estimators, and combines datasets.
result An optimal estimator outperforms the backdoor and frontdoor estimators by an unbounded constant factor.
Proposes clustering and pruning to simplify causal data fusion models.
problem Combining observational and experimental data to identify causal effects.
method Generalizes pruning and clustering operations for multiple data sources.
result Derives conditions for inferring causal effects from simplified models.
Estimates causal effects using neural autoregressive density estimators.
problem Estimating causal effects in non-linear systems.
method Neural autoregressive density estimators within Pearl's do-calculus framework.
result Retrieves causal effects from non-linear systems without explicit modeling.
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approachstat.ML
Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete graphical criteria and procedures exist for many identification problem…
Revisits causal inference identifiability with positivity assumption.
problem General identifiability in causal inference without positivity assumption.
method Introduces new algorithm sound and complete under positivity assumption.
result New algorithm connects general identifiability to classical identifiability.
CCHM algorithm learns BN structure with latent variables, improving causal effect measurement.
problem Latent variables cause spurious relationships in BN structure learning.
method Hybrid approach combining constraint-based and score-based learning, incorporating do-calculus.
result CCHM outperforms state-of-the-art in reconstructing true BN structure.
COTA learns abstraction maps from data without complete SCM knowledge.
problem Learning causally consistent representations at different resolutions.
method Multi-marginal Optimal Transport (OT) with do-calculus constraints and interventional cost.
result COTA outperforms non-causal and independent formulations on synthetic and real-world problems.
New approach for estimating individual treatment effects in low compliance settings.
problem Estimating individual treatment effects in scenarios with low compliance.
method Proposes a new approach using Structural Causal Model and do-calculus to estimate Individual Prescription Effect (IPE) with asymptotic variance guarantees.
result Consistently improves state-of-the-art in low compliance settings.
A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Modelstat.ME
Novel approach to compute hazard ratios from observational studies using SCMs and backdoor adjustment.
problem Identifying causal relationships from observational data using hazard ratios.
method Backdoor adjustment through structural causal models (SCMs) and do-calculus.
result Novel approach for computing hazard ratios from observational studies.
Hierarchical Causal Modelsstat.ME
Hierarchical causal models help understand cause and effect in nested data.
problem Learning cause and effect from nested hierarchical data.
method Extend structural causal models and causal graphical models with inner plates, develop graphical identification technique and estimation methods.
result Hierarchical data can enable causal identification even when non-hierarchical data cannot.
Unified framework for causal models at different levels of abstraction.
problem Relating causal models at varying levels of abstraction.
method Categorical framework using natural transformations between Markov functors.
result Generalized and unified causal abstractions with categorical proofs.
This study uses causal Shapley values to analyze how socioeconomic factors cause the spread of COVID-19.
problem Understanding how socioeconomic factors cause the spread of COVID-19.
method The study employs an explanatory framework from cooperative game theory augmented with do calculus, specifically causal Shapley values, to analyze the causal connections.
result The causal Shapley values reveal distinct advantages of non-linear machine learning models over linear models in multivariate analysis.
AP-Calculus offers a new framework for causal inference in Bayesian networks.
problem Causal inference in Bayesian networks with complex architectures.
method Introduces Attribution Projection Calculus (AP-Calculus) to determine causal relationships.
result Proves that for each label, exactly one intermediate node acts as a deconfounder.
Identifies causal effects in partially directed acyclic graphs with observed variables.
problem Identifying conditional causal effects in graphs with background knowledge and observed variables.
method Three results: identification formula, do calculus generalization, and algorithm completeness.
result Complete algorithm for identifying conditional effects in MPDAGs.
Obtaining a non-parametric expression for an interventional distribution is one of the most fundamental tasks in causal inference. Such an expression can be obtained for an identifiable causal effect by an algorithm or by manual application of do-calculus. Often we are left with a complicated expression which can lead …
We extend Andersson-Madigan-Perlman chain graphs by (i) relaxing the semidirected acyclity constraint so that only directed cycles are forbidden, and (ii) allowing up to two edges between any pair of nodes. We introduce global, and ordered local and pairwise Markov properties for the new models. We show the equivalence…
A new FFT-based method simplifies causal structure recovery for linear dynamical systems.
problem Efficiently identifying dynamic causal effects from time-series data.
method FFT-based approach to reduce computational complexity to .
result Significant computational advantage for graph reconstruction.
We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear structural causal models that allow for cycles, latent confounders and arbitrary probability distributions. We also generalize adjustment c…
BCDAG learns causal DAGs from Gaussian data using MCMC.
problem Learning causal DAGs from Gaussian observational data.
method Bayesian approach using MCMC for scalability and convergence diagnostics.
result Efficient scalability with observations and variables.
Causal Bayesian Optimizationstat.ML
Causal Bayesian Optimization improves global optimization with causal information.
problem Optimizing a system with causal relationships among variables.
method Combines causal inference, uncertainty quantification, and sequential decision making.
result Causal information significantly improves optimization strategies and reduces costs.
Develops a new framework for conditional independence.
problem Generalizing previous notions of conditional independence.
method Introduces transition probability spaces and transitional random variables.
result Satisfies all desired relevance relations except symmetry.
Geometric Causal Modelsstat.ML
Develops geometric causal models for causal inference from dependent data.
problem Causal inference from structured, dependent data (e.g., spatial, network, molecular).
method Geometric causal models (GCMs) exploiting symmetries of data generating process, combining group theory, ergodic theory, and Bayesian inference.
result Establishes identification and estimation of causal effects from dependent data.
Paper axiomatizes interventional probability distributions.
problem Causal inference and intervention.
method Axiomatization of interventional families.
result Markovian property of intervened distributions.
AI needs causal inference to avoid being just a correlation machine.
problem AI's inability to distinguish correlation from causation.
method Develops a unified framework connecting various causal statistical estimators and proves a Statistical Necessity Theorem for causal generalization.
result AI systems without causal grounding are brittle and biased, highlighting the need for causal statistics.
Algorithm BGLM-OFU minimizes regret in combinatorial causal bandits with binary models.
problem Minimizing expected regret in combinatorial causal bandits with binary generalized linear models.
method BGLM-OFU algorithm based on maximum likelihood estimation for Markovian BGLMs, and causal inference techniques for linear models with hidden variables.
result Achieves regret for binary generalized linear models.
It is common practice in using regression type models for inferring causal effects, that inferring the correct causal relationship requires extra covariates are included or ``adjusted for''. Without performing this adjustment erroneous causal effects can be inferred. Given this phenomenon it is common practice to inclu…