### Speaker

Jin Tian
(Iowa State University)

### Description

We investigate the problem of bounding counterfactual queries from an arbitrary collection of observational and experimental distributions and qualitative knowledge about the underlying data-generating model represented in the form of a causal diagram. We show that all counterfactual distributions in an arbitrary structural causal model (SCM) with finite discrete endogenous variables could be generated by a family of SCMs with the same causal diagram where unobserved (exogenous) variables are discrete with a finite domain. Utilizing this family of SCMs, we translate the problem of bounding counterfactuals into that of polynomial programming whose solution provides optimal bounds for the counterfactual query.

### External references

- 23040118
- 8f53138d-c5ee-44ab-82ca-4c87d90bee98
- fe9ebad6-b106-4588-a96f-bbe7b266a63a