Causal inference using global forecasting models for counterfactual prediction

Priscila Grecov, Kasun Bandara, Christoph Bergmeir, Klaus Ackermann, Samuel C. Campbell, Deborah Scott, Dan Ian Lubman

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

This research proposes a global forecasting and inference method based on recurrent neural networks (RNN) to predict policy interventions’ causal effects on an outcome over time through the counterfactual approach. The traditional univariate methods that operate within the well-established synthetic control method have strong linearity assumptions in the covariates. This has recently been addressed by successfully using univariate RNNs for this task. We use an RNN trained not univariately per series but globally across all time series, which allows us to model treated and control time series simultaneously over the pre-treatment period. Therewith, we do not need to make equivalence assumptions between distributions of the control and treated outcomes in the pre-treatment period. This allows us to achieve better accuracy and precisely isolate the effect of an intervention. We compare our novel approach with local univariate approaches on two real-world datasets on 1) how policy changes in Alcohol outlet licensing affect emergency service calls, and 2) how COVID19 lockdown measures affect emergency services use. Our results show that our novel method can outperform the accuracy of state-of-the-art predictions, thereby estimating the size of a causal effect more accurately. The experimental results are statistically significant, indicating our framework generates better counterfactual predictions.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part II
EditorsKamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty
Place of PublicationCham Switzerland
PublisherSpringer
Pages282-294
Number of pages13
Volume2
Edition1st
ISBN (Electronic)9783030757656
ISBN (Print)9783030757649
DOIs
Publication statusPublished - 2021
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2021 - Virtual, Delhi, India
Duration: 11 May 202114 May 2021
Conference number: 25th
https://www.pakdd2021.org (Website)
https://link.springer.com/book/10.1007/978-3-030-75765-6 (Proceedings)

Conference

ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2021
Abbreviated titlePAKDD-2021
CountryIndia
CityDelhi
Period11/05/2114/05/21
Internet address

Keywords

  • Global forecasting
  • Causal inference
  • Counterfactual

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