RealCause provides a realistic benchmark for causal inference.
problem Lack of a reliable benchmark for comparing causal effect estimators.
method Flexible generative models to create a benchmark that is both ground-truth and realistic.
result Evaluation of over 1500 causal estimators provides evidence for choosing hyperparameters using predictive metrics.
Bayesian model averaging improves causal effect estimation by averaging over multiple models.
problem Estimating causal effects under linear Structural Causal Models (SCMs).
method Bayesian model averaging using Gaussian scale mixture distributions for computational efficiency.
result Bayesian model averaging is optimal for causal effect estimation.
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.
BBCI uses meta prediction to estimate causal effects from datasets.
problem Estimating causal effects from observed data.
method Meta prediction to learn causal effect estimation.
result BBCI accurately estimates ATEs and CATEs across various causal inference problems.
Estimates causal effects in Gaussian Linear SCMs with finite data.
problem Estimating causal effects from observational data with latent confounders.
method Centralized Gaussian Linear SCMs (CGL-SCMs) and EM-based estimation algorithm.
result Learned CGL-SCM parameters accurately recover causal distributions from finite observational samples.
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.
Despite the major advances taken in causal modeling, causality is still an unfamiliar topic for many statisticians. In this paper, it is demonstrated from the beginning to the end how causal effects can be estimated from observational data assuming that the causal structure is known. To make the problem more challengin…
CICME estimates common and domain-specific causal mechanisms from multi-sensor data.
problem Inferring causal mechanisms from heterogeneous multi-sensor data across multiple domains.
method Three-step approach using Causal Transfer Learning (CTL).
result CICME reliably detects domain-invariant causal mechanisms and guides individual domain causal mechanism estimation.
Causal inference from observational data is hard due to discontinuous causal effects.
problem Causal inference from observational data is hard due to discontinuous causal effects.
method The problem is tackled by showing that many standard point estimates can be read as point summaries of multimodal distributions over the space of structural causal models.
result Many standard point estimates can be discontinuous summaries, while explicit posterior means and medians are continuous.
New algorithm estimates causal effects for non-Gaussian data.
problem Estimating causal effects in non-Gaussian distributions.
method Generalized k-Triangle Faithfulness Assumption and Edge Estimation Algorithm.
result Uniformly consistent estimates of causal effects.
New framework for dynamic causal graph modeling and effect estimation.
problem Dynamic changes in causal relationships over time.
method Score-based causal discovery with autoregressive model structure.
result Dynamic causal graph with time-varying causal relations.
This paper discusses the problem of causal query in observational data with hidden variables, with the aim of seeking the change of an outcome when "manipulating" a variable while given a set of plausible confounding variables which affect the manipulated variable and the outcome. Such an "experiment on data" to estima…
New method estimates causal effects of multiple versions of treatment.
problem Ignoring multiple versions of treatment leads to biased causal effect estimates.
method Mixture-of-Experts framework for estimating version-specific causal effects.
result Effective method for estimating causal effects of latent versions.
Paper identifies and estimates CAPCEs in continuous treatment settings.
problem Estimating heterogeneous causal effects of continuous treatments.
method Instrumental variable approach to identify CAPCEs under weaker conditions.
result Developed three families of CAPCE estimators with statistical properties analyzed.
SCBMs model causal effects using low-dimensional bottlenecks.
problem Causal effect estimation in high-dimensional systems.
method Structural causal models with low-dimensional summary statistics.
result SCBMs provide a flexible framework for task-specific dimension reduction.
SNAP efficiently identifies causal effects without needing full graph learning.
problem Efficiently estimating causal effects on a subset of variables.
method Sequential Non-Ancestor Pruning (SNAP) framework.
result SNAP reduces independence tests and computation time without sacrificing causal effect estimations.
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.
Study functional confounders in causal inference, enabling estimable effects.
problem Causal inference challenges with functional confounders violating positivity.
method Functional interventions, functional positivity, gradient fields, Level-set Orthogonal Descent Estimation (LODE).
result Valid causal effect estimation under certain conditions.
SD-SCMs generate counterfactual data for causal inference benchmarks.
problem Benchmarking causal inference methods with realistic data.
method Sequence-driven structural causal models (SD-SCMs) for causal inference.
result State-of-the-art methods struggle with individual treatment effect estimation.
Survey of deep causal models for industrial applications.
problem Estimating causal effects using deep learning.
method Deep causal models map covariates to a representation space and use objective functions for unbiased counterfactual data estimation.
result Comprehensive overview of deep causal models with industry applications.
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.
Method estimates bivariate causal models using normalising flows and variational Gaussian process regression.
problem Lack of explainability in AI models, especially in causal mechanisms.
method Combination of normalising flows for density estimation and variational Gaussian process regression for post-nonlinear models.
result Method better explains cause-effect pairs than simple additive noise models.
NICE model estimates causal effects for image treatments.
problem Challenges in causal effect estimation for multi-dimensional treatments.
method Proposes NICE model for image treatments, incorporating rich multidimensional information.
result NICE significantly outperforms existing models in estimating causal effects for image treatments.
SPOT improves differentiable causal discovery by estimating skeleton posterior for latent confounders.
problem Scalable and accurate estimation of causal skeletons in the presence of latent confounders.
method SPOT (Skeleton Posterior-guided OpTimization) framework that estimates skeleton posterior and integrates it with differentiable causal discovery.
result SPOT enhances differentiable causal discovery by reducing the search space and improving accuracy.
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.
New method learns unbiased treatment representations from structured high-dimensional data.
problem Estimating causal effects from high-dimensional, structured treatments.
method Contrastive learning approach to learn unbiased treatment representations.
result The method identifies causal factors and discards non-causal ones, leading to unbiased causal effect estimates.
New approach uses negative controls to estimate causal parameters without completeness conditions.
problem Estimating causal parameters when not all confounders are observed.
method Identification strategy based on minimax learning formulations for general function classes.
result Avoids completeness conditions and uniqueness assumptions on bridge functions.
New tests for distributional causal effects using improved kernel estimators.
problem Testing for higher-order moments and multidimensional outcomes affected by treatment.
method Improved kernel estimators based on doubly robust mean embeddings.
result New permutation-based tests for distributional causal effects with improved convergence rates.
Single proxy variable helps estimate causal effects from confounders.
problem Estimating causal effects from treatment to outcome when unobserved confounders are present.
method Assumes a single, potentially multi-dimensional proxy variable of the unobserved confounder and a known mechanism generating the proxy from the confounder. Proves causal effects are identifiable under completeness assumption.
result Causal effects are identifiable under SPICE assumption.
We consider basic conceptual questions concerning the relationship between statistical estimation and causal inference. Firstly, we show how to translate causal inference problems into an abstract statistical formalism without requiring any structure beyond an arbitrarily-indexed family of probability models. The forma…
In this work, we formalize the problem of causal inference over graph-based relational time-series data where each node in the graph has one or more time-series associated to it. We propose causal inference models for this problem that leverage both the graph topology and time-series to accurately estimate local causal…
CausalPFN automates causal effect estimation from observational data.
problem Manual selection of causal effect estimators is time-consuming and requires domain expertise.
method CausalPFN is a transformer that learns to infer causal effects from raw observations without task-specific adjustments.
result CausalPFN achieves superior performance on various benchmarks and real-world tasks.
Proposes a Bayesian framework for causal inference without explicit likelihood modeling.
problem Challenges in principled Bayesian inference for causal effects.
method Generalized Bayesian framework that places priors directly on causal estimands and updates using identification-driven loss functions.
result Yields generalized posteriors for causal effects with uncertainty quantification.
New approach for causal inference with interdependent, time-varying latent confounders.
problem Estimating causal effects with interdependent, time-varying latent confounders.
method Variational estimation with a representer theorem and random input space.
result Demonstrates effectiveness on various temporal datasets.
Method estimates causal effects from incremental data, overcoming missing data challenges.
problem Estimating causal effects from non-stationary, incrementally available observational data.
method Continual Causal Effect Representation Learning
result Method achieves continual causal effect estimation without compromising original data.
Deep learning aids causal inference in complex settings.
problem Estimating heterogeneous treatment effects in non-linear, time-varying, and encoded confounders.
method Intuitive introduction to deep learning and causal inference, focusing on observational data.
result Maximizes accessibility to causal inference through deep learning.
Estimates causal effect using proxies in multi-domain settings.
problem Estimating causal effect in settings with unobserved confounders across domains.
method Proposes estimation techniques using proxy variables for discrete or categorical data.
result Proves identifiability and consistency of causal effect estimation.
New method prevents invalid inference after causal discovery.
problem Invalid inference after causal discovery.
method Developed tools for valid post-causal-discovery inference.
result Our method provides reliable coverage while achieving more accurate causal discovery.
New algorithm improves causal effect estimation for continuous treatments.
problem Observational causal inference with continuous treatments.
method End-to-end entropy balancing for maximizing causal inference accuracy.
result Our algorithm estimates causal effect more accurately than baseline.
Deep learning improves causal effect estimation from complex observational data.
problem Estimating causal effects from complex observational data with low bias.
method Unified deep learning framework using multitask recurrent neural networks.
result Deep learning estimator shows lower bias in causal effect estimates.
New method estimates causal effects without knowing graph structure.
problem Estimating causal effects when graph structure is unknown.
method Testable conditional independence statements for front-door adjustment.
result Effect estimation without Markov equivalence class knowledge.
Estimating individual treatment effects from data of randomized experiments is a critical task in causal inference. The Stable Unit Treatment Value Assumption (SUTVA) is usually made in causal inference. However, interference can introduce bias when the assigned treatment on one unit affects the potential outcomes of t…
Study estimates heterogeneous principal causal effects with binary treatments and intermediate variables.
problem Estimating subgroup effects within strata defined by potential values of an intermediate variable.
method Proposes a framework for estimating and forming confidence intervals for heterogeneous principal causal effects under principal ignorability assumption. Develops several estimators with varying robustness properties.
result Established large-sample theory and analyzed bias contributions of each approach.
Study develops method for estimating causal effects in continuous variables.
problem Lack of methods for estimating causal effects in continuous variables.
method Develops a method independent of data generating models for continuous variable interventions.
result Preserves identifiability of data and applies to any generating models.
New method balances covariates for stable causal survival effect estimation.
problem Estimating causal survival effects in data with conditionally-independent censoring.
method Covariate-balancing approach to empirically stable and asymptotically efficient estimation.
result Validated theoretical results in synthetic and semi-synthetic data.
Paper tackles estimating individual treatment effects from observational data.
problem Estimating the difference between outcomes with and without treatment from single observation.
method Formulated as inference from hidden variables, uses a model of four causal populations, proposes ECM algorithm.
result ECM algorithm provides better performance compared to baseline methods on synthetic and real-world data.
The paper tackles causal rule discovery from observational data.
problem Challenges in inferring causal effects from observational data due to confounding factors and high variance.
method Measures causal effect from observational data, providing graphical criteria and a conservative estimator.
result Proposes an efficient algorithm that maximises the estimator and discovers meaningful causal rules.
Estimate collapsibility of causal effects in CPDAGs via strong d-convex hulls.
problem Estimate causal effects in CPDAGs.
method Use strong d-convex hulls to characterize minimal collapsible sets.
result Efficient algorithm for obtaining collapsible sets in DAGs and CPDAGs.