Causal Transformer for Estimating Counterfactual Outcomes Article Swipe
Valentyn Melnychuk
,
Dennis Frauen
,
Stefan Feuerriegel
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.07258
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.07258
Estimating counterfactual outcomes over time from observational data is relevant for many applications (e.g., personalized medicine). Yet, state-of-the-art methods build upon simple long short-term memory (LSTM) networks, thus rendering inferences for complex, long-range dependencies challenging. In this paper, we develop a novel Causal Transformer for estimating counterfactual outcomes over time. Our model is specifically designed to capture complex, long-range dependencies among time-varying confounders. For this, we combine three transformer subnetworks with separate inputs for time-varying covariates, previous treatments, and previous outcomes into a joint network with in-between cross-attentions. We further develop a custom, end-to-end training procedure for our Causal Transformer. Specifically, we propose a novel counterfactual domain confusion loss to address confounding bias: it aims to learn adversarial balanced representations, so that they are predictive of the next outcome but non-predictive of the current treatment assignment. We evaluate our Causal Transformer based on synthetic and real-world datasets, where it achieves superior performance over current baselines. To the best of our knowledge, this is the first work proposing transformer-based architecture for estimating counterfactual outcomes from longitudinal data.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2204.07258
- https://arxiv.org/pdf/2204.07258
- OA Status
- green
- Cited By
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224002928
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4224002928Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2204.07258Digital Object Identifier
- Title
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Causal Transformer for Estimating Counterfactual OutcomesWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-04-14Full publication date if available
- Authors
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Valentyn Melnychuk, Dennis Frauen, Stefan FeuerriegelList of authors in order
- Landing page
-
https://arxiv.org/abs/2204.07258Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2204.07258Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2204.07258Direct OA link when available
- Concepts
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Counterfactual thinking, Computer science, Transformer, Machine learning, Observational study, Confounding, Artificial intelligence, Econometrics, Data mining, Mathematics, Statistics, Engineering, Psychology, Voltage, Social psychology, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 6, 2023: 8, 2021: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.assignment. | 135 |
| abstract_inverted_index.confounding | 111 |
| abstract_inverted_index.covariates, | 75 |
| abstract_inverted_index.performance | 151 |
| abstract_inverted_index.subnetworks | 69 |
| abstract_inverted_index.transformer | 68 |
| abstract_inverted_index.treatments, | 77 |
| abstract_inverted_index.Transformer. | 99 |
| abstract_inverted_index.applications | 12 |
| abstract_inverted_index.architecture | 168 |
| abstract_inverted_index.challenging. | 34 |
| abstract_inverted_index.confounders. | 62 |
| abstract_inverted_index.dependencies | 33, 59 |
| abstract_inverted_index.longitudinal | 174 |
| abstract_inverted_index.personalized | 14 |
| abstract_inverted_index.specifically | 53 |
| abstract_inverted_index.time-varying | 61, 74 |
| abstract_inverted_index.Specifically, | 100 |
| abstract_inverted_index.observational | 6 |
| abstract_inverted_index.counterfactual | 1, 46, 105, 171 |
| abstract_inverted_index.non-predictive | 130 |
| abstract_inverted_index.representations, | 119 |
| abstract_inverted_index.state-of-the-art | 17 |
| abstract_inverted_index.cross-attentions. | 87 |
| abstract_inverted_index.transformer-based | 167 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.90353345 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |