Papers by Eli Ben-Michael
Text-Transport: Toward Learning Causal Effects of Natural Language (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for causal inference require strong assumptions about the data, meaning the data from which one *can* estimate valid causal effects is not representative of the actual target domain of interest. |
| Approach: | They propose a method for estimation of causal effects from natural language under any text distribution using the notion of distribution shift. |
| Outcome: | The proposed method can be used to estimate causal effects from natural language under any text distribution. |