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Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Modeling Treatment Effect with Cross-Domain Data

Authors : Bin Han, Ya-Lin Zhang, Lu Yu, Biying Chen, Longfei Li, Jun Zhou

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer Nature Singapore

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Abstract

Treatment effect estimation has received increasing attention recently. However, the issue of data sparsity often poses a significant challenge, limiting the feasibility of modeling. This paper aims to leverage cross-domain data to mitigate the data sparsity issue, and presents a framework called TEC. TEC incorporates a collaborative and adversarial generalization module to enhance information sharing and transferability across domains. This module encourages the learned representations of different domains to be more cohesive, thereby improving the generalizability of the models. Furthermore, we address the issue of poor performance for few-shot samples in each domain, and propose a pattern augmentation module that explicitly borrows samples from other domains and applies the self-teaching philosophy to them. Extensive experiments are conducted on both synthetic and benchmark datasets to demonstrate the superiority of the proposed framework.

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previous chapter LPSD: Low-Rank Plus Sparse Decomposition for Highly Compressed CNN Models
Footnotes
1
We use a classification threshold of 0.5 for explanation purposes.
 
2
Due to commercial confidentiality, we omit some details here and below.
 
Literature
1.
2.
Battocchi, K., et al.: EconML: A python package for ml-based heterogeneous treatment effects estimation (2019)
3.
Bica, I., van der Schaar, M.: Transfer learning on heterogeneous feature spaces for treatment effects estimation. In: NeurIPS (2022)
4.
Dahabreh, I.J., et al.: Study designs for extending causal inferences from a randomized trial to a target population. Am. J. Epidemiol. 190(8), 1632–1642 (2021)CrossRef
5.
Dorie, V., Hill, J., Shalit, U., Scott, M., Cervone, D.: Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition. Stat. Sci. 34(1), 43–68 (2019)MathSciNetCrossRef
6.
Hassanpour, N., Greiner, R.: Learning disentangled representations for counterfactual regression. In: ICLR (2020)
7.
Hatt, T., Berrevoets, J., Curth, A., Feuerriegel, S., van der Schaar, M.: Combining observational and randomized data for estimating heterogeneous treatment effects (2022). arXiv:​2202.​12891
8.
Huang, Q., Ma, J., Li, J., Sun, H., Chang, Y.: SemiiTE: semi-supervised individual treatment effect estimation via disagreement-based co-training. In: ECML PKDD, pp. 400–417 (2023)
9.
Johansson, F., Shalit, U., Sontag, D.: Learning representations for counterfactual inference. In: ICML, pp. 3020–3029 (2016)
10.
11.
Künzel, S.R., Sekhon, J.S., Bickel, P.J., Yu, B.: Meta-learners for estimating heterogeneous treatment effects using machine learning. PNAS 116(10), 4156–4165 (2019)CrossRef
12.
Kyono, T., Bica, I., Qian, Z., van der Schaar, M.: Selecting treatment effects models for domain adaptation using causal knowledge. Health 4(2), 1–29 (2023)
13.
Pearl, J.: Causality. Cambridge University Press, Cambridge (2009)
14.
Powers, S., et al.: Some methods for heterogeneous treatment effect estimation in high-dimensions. Stat. Med. 37(11), 1767–1787 (2018)MathSciNetCrossRef
15.
Rosenbaum, P.R.: Model-based direct adjustment. JASA 82(398), 387–394 (1987)CrossRef
16.
Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70(1), 41–55 (1983)MathSciNetCrossRef
17.
Rubin, D.B.: Causal inference using potential outcomes: design, modeling, decisions. JASA 100(469), 322–331 (2005)MathSciNetCrossRef
18.
Shalit, U., Johansson, F.D., Sontag, D.: Estimating individual treatment effect: generalization bounds and algorithms. In: ICML, pp. 3076–3085 (2017)
19.
Shi, C., Blei, D., Veitch, V.: Adapting neural networks for the estimation of treatment effects. In: NeurIPS, vol. 32 (2019)
20.
Sun, Y., Zhang, Y., Wang, W., Li, L., Zhou, J.: Treatment effect estimation across domains. In: CIKM, pp. 2352—2361 (2023)
21.
Tang, C., et al.: Debiased causal tree: heterogeneous treatment effects estimation with unmeasured confounding. In: NeurIPS, vol. 35 (2022)
22.
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: NeurIPS, vol. 30 (2017)
23.
Yao, L., Chu, Z., Li, S., Li, Y., Gao, J., Zhang, A.: A survey on causal inference. TKDD 15(5), 1–46 (2021)CrossRef
24.
Yao, L., Li, S., Li, Y., Huai, M., Gao, J., Zhang, A.: Representation learning for treatment effect estimation from observational data. In: NeurIPS, vol. 31 (2018)
25.
Zhang, Y., Zhou, J., Shi, Q., Li, L.: Exploring the combination of self and mutual teaching for tabular-data-related semi-supervised regression. ESWA 213, 118931 (2023)
26.
Zhang, Y., et al.: A backcasting framework for approximating macro-outcome via micro-treatment. SSRN 4494664 (2023)
Metadata
Title
Modeling Treatment Effect with Cross-Domain Data
Authors
Bin Han
Ya-Lin Zhang
Lu Yu
Biying Chen
Longfei Li
Jun Zhou
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-981-9722-41-9

Electronic ISBN: 978-981-9722-42-6

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-2242-6_29

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