A Practical Data Repository for Causal Learning with Big Data

Cheng, Lu; Guo, Ruocheng; Moraffah, Raha; Candan, K. Selçuk; Raglin, Adrienne; Liu, Huan

doi:10.1007/978-3-030-49556-5_23

Cited by 11 publications

(15 citation statements)

References 19 publications

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“…Below, we briefly introduce exemplar benchmarking datasets in each category. For a comprehensive description, please refer to [18]. We first introduce data used under the unconfoundedness assumption.…”

Section: Benchmarking Datasetsmentioning

confidence: 99%

“…The conventional way to understand causality is to use interventions and/or randomized controlled trials (RCTs) [15,16]. In many situations, however, these are time-consuming, impractical, or sometimes even unethical [17,16,18]. Attention has been drawn to the recent availability of big observational data in all walks of life as they provide new opportunities for learning causality, without the disadvantages of RCTs.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation Methods and Measures for Causal Learning Algorithms

Liu¹,

Guo²,

Moraffah³

et al. 2022

Preprint

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Section: Benchmarking Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation Methods and Measures for Causal Learning Algorithms

Liu¹,

Guo²,

Moraffah³

et al. 2022

Preprint

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“…We evaluate the models on two benchmark datasets [8] for causal effect estimation-Infant Health and Development Program (IHDP) [18] and News [23]. For both datasets, we use the original context, treatment assignment and synthetic outcomes (more details later) that reflect the sequential observations in long-term causal inference.…”

Section: Experimental Designmentioning

confidence: 99%

Long-Term Effect Estimation with Surrogate Representation

Cheng

Guo

Liu

2021

Proceedings of the 14th ACM International Conference on Web Search and Data Mining

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There are many scenarios where short-and long-term causal effects of an intervention are different. For example, low-quality ads may increase short-term ad clicks but decrease the long-term revenue via reduced clicks; search engines measured by inappropriate performance metrics may increase search query shares in a short-term but not long-term. This work therefore studies the long-term effect where the outcome of primary interest, or primary outcome, takes months or even years to accumulate. The observational study of long-term effect presents unique challenges. First, the confounding bias causes large estimation error and variance, which can further accumulate towards the prediction of primary outcomes. Second, short-term outcomes are often directly used as the proxy of the primary outcome, i.e., the surrogate. Notwithstanding its simplicity, this method entails the strong surrogacy assumption that is often impractical. To tackle these challenges, we propose to build connections between long-term causal inference and sequential models in machine learning. This enables us to learn surrogate representations that account for the temporal unconfoundedness and circumvent the stringent surrogacy assumption by conditioning on time-varying confounders in the latent space. Experimental results show that the proposed framework outperforms the state-of-the-art.

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“…A comprehensive review of these methods can be found in [Guo et al, 2020a]. In [Cheng et al, 2019], a review of the datasets and metrics for evaluation of ITE estimation is presented. However, this line of work does not consider to utilize network information for learning causal effects.…”

Section: Related Workmentioning

confidence: 99%

IGNITE: A Minimax Game Toward Learning Individual Treatment Effects from Networked Observational Data

Guo

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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Networked observational data presents new opportunities for learning individual causal effects, which plays an indispensable role in decision making. Such data poses the challenge of confounding bias. Previous work presents two desiderata to handle confounding bias. On the treatment group level, we aim to balance the distributions of confounder representations. On the individual level, it is desirable to capture patterns of hidden confounders that predict treatment assignments. Existing methods show the potential of utilizing network information to handle confounding bias, but they only try to satisfy one of the two desiderata. This is because the two desiderata seem to contradict each other. When the two distributions of confounder representations are highly overlapped, then we confront the undiscriminating problem between the treated and the controlled. In this work, we formulate the two desiderata as a minimax game. We propose IGNITE that learns representations of confounders from networked observational data, which is trained by a minimax game to achieve the two desiderata. Experiments verify the efficacy of IGNITE on two datasets under various settings.

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A Practical Data Repository for Causal Learning with Big Data

Cited by 11 publications

References 19 publications

Evaluation Methods and Measures for Causal Learning Algorithms

Evaluation Methods and Measures for Causal Learning Algorithms

Long-Term Effect Estimation with Surrogate Representation

IGNITE: A Minimax Game Toward Learning Individual Treatment Effects from Networked Observational Data

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