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Found 9 papers, 4 papers with code
Testing Generalizability in Causal Inference
By basing simulations on real data, our method ensures more realistic evaluations, which is often missing in current work relying on simplified datasets.
Marginal Causal Flows for Validation and Inference
Investigating the marginal causal effect of an intervention on an outcome from complex data remains challenging due to the inflexibility of employed models and the lack of complexity in causal benchmark datasets, which often fail to reproduce intricate real-world data patterns.
Results on Counterfactual Invariance
In this paper we provide a theoretical analysis of counterfactual invariance.
PWSHAP: A Path-Wise Explanation Model for Targeted Variables
We introduce Path-Wise Shapley effects (PWSHAP), a framework for assessing the targeted effect of a binary (e. g.~treatment) variable from a complex outcome model.
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects
These improved estimators are inspired by doubly robust estimators of the causal mean, using a similar form within the kernel space.
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment
Causal inference grows increasingly complex as the number of confounders increases.
Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams
We consider the problem of structure learning for bow-free acyclic path diagrams (BAPs).
Margins of discrete Bayesian networks
Bayesian network models with latent variables are widely used in statistics and machine learning.
Sparse Nested Markov models with Log-linear Parameters
To make modeling and inference with nested Markov models practical, it is necessary to limit the number of parameters in the model, while still correctly capturing the constraints in the marginal of a DAG model.
