Transfer Learning for Estimating Causal Effects using Neural Networks
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Abstract (arXiv)
We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the same underlying causal mechanisms. We compare our algorithms with those in the extant literature using extensive simulation studies based on large-scale voter persuasion experiments and the MNIST database. Our methods can perform an order of magnitude better than existing benchmarks while using a fraction of the data.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Aug 2018.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 5; 2024: 0; 2025: 0; 2026: 3.
13 papers.
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- 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimationcited by 21
- 2021Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysiscited by 14
- 2023Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimationcited by 11
- 2022Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regressioncited by 4
- 2023Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time seriescited by 4
- 2023Transfer Learning for Causal Effect Estimationcited by 3
- 2023Meta-learning for heterogeneous treatment effect estimation with closed-form solverscited by 2
- 2022BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effectcited by 1
- 2023Assessing Electricity Service Unfairness with Transfer Counterfactual Learningcited by 1
- 2022A Survey on Causal Representation Learning and Future Work for Medical Image Analysiscited by 0
- 2026Transfer learning for causal forestcited by 0
- 2026Transportable Causal Effect Estimation across Networks under Interferencecited by 0
- 2026Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomes5 Sep 2026
- 2026Transportable Causal Effect Estimation across Networks under Interference19 Aug 2026
- 2026Transfer learning for causal forest5 Jun 2026
- 2023Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series24 Oct 2023
- 2023Assessing Electricity Service Unfairness with Transfer Counterfactual Learning5 Oct 2023
- 2023Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation25 May 2023
- 2023Meta-learning for heterogeneous treatment effect estimation with closed-form solvers19 May 2023
- 2023Transfer Learning for Causal Effect Estimation16 May 2023
- 2022BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect19 Nov 2022
- 2022A Survey on Causal Representation Learning and Future Work for Medical Image Analysis28 Oct 2022
- 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation8 Oct 2022
- 2022Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression19 Jul 2022
1 more
- 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimationcited by 21
- 2021Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysiscited by 14
- 2023Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimationcited by 11
- 2022Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regressioncited by 4
- 2023Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time seriescited by 4
- 2023Transfer Learning for Causal Effect Estimationcited by 3
- 2023Meta-learning for heterogeneous treatment effect estimation with closed-form solverscited by 2
- 2022BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effectcited by 1
- 2023Assessing Electricity Service Unfairness with Transfer Counterfactual Learningcited by 1
- 2022A Survey on Causal Representation Learning and Future Work for Medical Image Analysiscited by 0
- 2026Transfer learning for causal forestcited by 0
- 2026Transportable Causal Effect Estimation across Networks under Interferencecited by 0
12 most cited of 13 All 13 (JSON)
- 2026Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomes5 Sep 2026
- 2026Transportable Causal Effect Estimation across Networks under Interference19 Aug 2026
- 2026Transfer learning for causal forest5 Jun 2026
- 2023Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series24 Oct 2023
- 2023Assessing Electricity Service Unfairness with Transfer Counterfactual Learning5 Oct 2023
- 2023Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation25 May 2023
- 2023Meta-learning for heterogeneous treatment effect estimation with closed-form solvers19 May 2023
- 2023Transfer Learning for Causal Effect Estimation16 May 2023
- 2022BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect19 Nov 2022
- 2022A Survey on Causal Representation Learning and Future Work for Medical Image Analysis28 Oct 2022
- 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation8 Oct 2022
- 2022Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression19 Jul 2022
12 newest of 13 All 13 (JSON)