# Nested Counterfactual Identification from Arbitrary Surrogate Experiments

@inproceedings{Correa2021NestedCI, title={Nested Counterfactual Identification from Arbitrary Surrogate Experiments}, author={Juan David Correa and Sanghack Lee and Elias Bareinboim}, booktitle={NeurIPS}, year={2021} }

The Ladder of Causation describes three qualitatively different types of activities an agent may be interested in engaging in, namely, seeing (observational), doing (interventional), and imagining (counterfactual) (Pearl and Mackenzie, 2018). The inferential challenge imposed by the causal hierarchy is that data is collected by an agent observing or intervening in a system (layers 1 and 2), while its goal may be to understand what would have happened had it taken a different course of action…

## 3 Citations

Partial Counterfactual Identification from Observational and Experimental Data

- Computer ScienceICML
- 2022

This paper shows that all counterfactual distributions in an arbitrary structural causal model (SCM) with discrete observed domains could be generated by a canonical family of SCMs with the same causal diagram where unobserved (exogenous) variables are also discrete, taking values in finite domains.

Counterfactual Transportability: A Formal Approach

- Computer ScienceICML
- 2022

This paper investigates the transportability of counterfactuals from an arbitrary combination of observational and experimental distributions coming from disparate domains and introduces a sufﬁcient and necessary graphical condition and develops an complete algorithm for transportingcounterfactual quantities across domains in nonparametric settings.

Causal Inference Through the Structural Causal Marginal Problem

- Computer ScienceICML
- 2022

This work formalises an approach to counterfactual inference based on merging information from multiple datasets using the response function formulation and shows that it reduces the space of allowed marginal and joint SCMs.

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