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Update README.md #17

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Oct 9, 2023
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Update README.md #17

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Expand Up @@ -95,7 +95,7 @@ _Pull requests welcome!_
| [Adjusting for confounding with text matching](https://scholar.princeton.edu/sites/default/files/bstewart/files/textmatching_preprint.pdf) <br> Margaret E Roberts, Brandon M Stewart, and Richard A Nielsen | Estimate a low-dimensional summary of the text and condition on this summary via matching to remove confouding. Proposes a method of text matching, topical inverse regression matching, that matches on both on the topical content and propensity score.||
| [Matching with text data: An experimental evaluation of methods for matching documents and of measuring match quality](https://arxiv.org/pdf/1801.00644) <br> Reagan Mozer, Luke Miratrix, Aaron Russell Kaufman, L Jason Anastasopoulos | Characterizes and empirically evaluates a framework for matching text documents that decomposes existing methods into: the choice of text representation, and the choice of distance metric.||
| [Learning representations for counterfactual inference](http://www.jmlr.org/proceedings/papers/v48/johansson16.pdf) <br> Fredrik Johansson, Uri Shalit, David Sontag | One of their semi-synthetic experiments has news content as a confounder. | |

| [Conceptualizing Treatment Leakage in Text-based Causal Inference](https://arxiv.org/pdf/2205.00465.pdf) <br> Adel Daoud, Connor T. Jerzak, and Richard Johansson | Characterize the problem of the leakage of treatment signal when controlling for text-based confounders which may lead to issues in identification and estimation. Simulation study on how treatment-leakage leads to issues with the estimation of the Average Treatment Effect (ATE) and how to mitigate this bias with text pre-processing by assuming separability.


# Causality to Improve NLP
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