Causal inference by using invariant prediction: identification and confidence intervals
Explore this paper's citation graph
Summary
This work proposes to exploit invariance of a prediction under a causal model for causal inference: given different experimental settings (e.g. various interventions) the authors collect all models that do show invariance in their predictive accuracy across settings and interventions, and yields valid confidence intervals for the causal relationships in quite general scenarios.
- Type
- preprint
- Published
- 2015-01-06
- Cited by
- 1,241
- References
- 163
- Access
- Open access
- OpenAlex
- https://openalex.org/W1905064697
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:36882285
Keywords
Causal inference, Causal model, Inference, Observational study, Econometrics
References
- Über eine Eigenschaft der normalen Verteilungsfunktion
- Causality, mediation and time: a dynamic viewpoint
- Statistics for High-Dimensional Data: Methods, Theory and Applications
- Introduction to Econometrics
- Principal causal effect identification and surrogate end point evaluation by multiple trials
- Causal Inference Using Potential Outcomes
- Exact Bayesian structure learning from uncertain interventions
- Local computations with probabilities on graphical structures and their application to expert systems
- A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships
- Are There Algorithms That Discover Causal Structure?
- Interventions and Causal Inference
- A Discovery Algorithm for Directed Cyclic Graphs
- Greedy function approximation: A gradient boosting machine.
- Parameter and Structure Learning in Nested Markov Models
- On robust testing for normality in chemometrics
- Functional Causal Mediation Analysis With an Application to Brain Connectivity
- CAUSAL LINKAGES AMONG SHANGHAI, SHENZHEN, AND HONG KONG STOCK MARKETS
- Identifiability of parameters in latent structure models with many observed variables
- Boosting the margin: A new explanation for the effectiveness of voting methods
- CAM: Causal Additive Models, high-dimensional order search and penalized regression
Cited by
- A Review of Some Recent Advances in Causal Inference
- Goals in Nutrition Science 2015–2020
- The many levels of causal brain network discovery: Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino and S.L. Bressler.
- Causal Transfer in Machine Learning
- Structure Learning in Graphical Modeling
- Model Criticism for Bayesian Causal Inference
- Joint Causal Inference on Observational and Experimental Datasets
- A Review on Algorithms for Constraint-based Causal Discovery
- Causal Discovery as Semi-Supervised Learning
- Learning Causal Effects From Many Randomized Experiments Using Regularized Instrumental Variables
- Causal Discovery Using Proxy Variables
- Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments
- Cost-Optimal Learning of Causal Graphs
- Estimating linear causality in the presence of latent variables
- A New Measure of Conditional Dependence for Causal Structural Learning
- The right tool for the right question --- beyond the encoding versus decoding dichotomy
- Causal Structure Learning
- Learning Causal Structures Using Regression Invariance
- Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions
- Invariant Causal Prediction for Nonlinear Models
Related papers
- Invariant Risk Minimization
- Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
- Confidence in causal discovery with linear causal models
- Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions
- Invariant Causal Prediction for Nonlinear Models
- A Generative Adversarial Framework for Bounding Confounded Causal Effects
- Inferring causal directions from uncertain data