End-To-End Causal Effect Estimation from Unstructured Natural Language Data Article Swipe
Nikita Dhawan
,
Leonardo Cotta
,
Karen Ullrich
,
Rahul G. Krishnan
,
Chris J. Maddison
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.07018
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.07018
Knowing the effect of an intervention is critical for human decision-making, but current approaches for causal effect estimation rely on manual data collection and structuring, regardless of the causal assumptions. This increases both the cost and time-to-completion for studies. We show how large, diverse observational text data can be mined with large language models (LLMs) to produce inexpensive causal effect estimates under appropriate causal assumptions. We introduce NATURAL, a novel family of causal effect estimators built with LLMs that operate over datasets of unstructured text. Our estimators use LLM conditional distributions (over variables of interest, given the text data) to assist in the computation of classical estimators of causal effect. We overcome a number of technical challenges to realize this idea, such as automating data curation and using LLMs to impute missing information. We prepare six (two synthetic and four real) observational datasets, paired with corresponding ground truth in the form of randomized trials, which we used to systematically evaluate each step of our pipeline. NATURAL estimators demonstrate remarkable performance, yielding causal effect estimates that fall within 3 percentage points of their ground truth counterparts, including on real-world Phase 3/4 clinical trials. Our results suggest that unstructured text data is a rich source of causal effect information, and NATURAL is a first step towards an automated pipeline to tap this resource.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.07018
- https://arxiv.org/pdf/2407.07018
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400519148
All OpenAlex metadata
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https://openalex.org/W4400519148Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.07018Digital Object Identifier
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End-To-End Causal Effect Estimation from Unstructured Natural Language DataWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-09Full publication date if available
- Authors
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Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, Chris J. MaddisonList of authors in order
- Landing page
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https://arxiv.org/abs/2407.07018Publisher landing page
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https://arxiv.org/pdf/2407.07018Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2407.07018Direct OA link when available
- Concepts
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Estimation, Natural (archaeology), Computer science, Unstructured data, Natural language, Natural language processing, Artificial intelligence, History, Data mining, Economics, Big data, Archaeology, ManagementTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.synthetic | 137 |
| abstract_inverted_index.technical | 115 |
| abstract_inverted_index.variables | 92 |
| abstract_inverted_index.approaches | 13 |
| abstract_inverted_index.automating | 123 |
| abstract_inverted_index.challenges | 116 |
| abstract_inverted_index.collection | 22 |
| abstract_inverted_index.estimation | 17 |
| abstract_inverted_index.estimators | 74, 86, 106, 166 |
| abstract_inverted_index.percentage | 178 |
| abstract_inverted_index.randomized | 152 |
| abstract_inverted_index.real-world | 187 |
| abstract_inverted_index.regardless | 25 |
| abstract_inverted_index.remarkable | 168 |
| abstract_inverted_index.appropriate | 62 |
| abstract_inverted_index.computation | 103 |
| abstract_inverted_index.conditional | 89 |
| abstract_inverted_index.demonstrate | 167 |
| abstract_inverted_index.inexpensive | 57 |
| abstract_inverted_index.assumptions. | 29, 64 |
| abstract_inverted_index.information, | 206 |
| abstract_inverted_index.information. | 132 |
| abstract_inverted_index.intervention | 5 |
| abstract_inverted_index.performance, | 169 |
| abstract_inverted_index.structuring, | 24 |
| abstract_inverted_index.unstructured | 83, 196 |
| abstract_inverted_index.corresponding | 145 |
| abstract_inverted_index.counterparts, | 184 |
| abstract_inverted_index.distributions | 90 |
| abstract_inverted_index.observational | 44, 141 |
| abstract_inverted_index.systematically | 158 |
| abstract_inverted_index.decision-making, | 10 |
| abstract_inverted_index.time-to-completion | 36 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |