The CausalImpact R package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian time-series model to estimate how the response metric might have evolved after the intervention if the intervention had not occurred.

References in zbMATH (referenced in 16 articles )

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  1. Chen, Aiyou; Au, Timothy C.: Robust causal inference for incremental return on ad spend with randomized paired geo experiments (2022)
  2. Menchetti, Fiammetta; Bojinov, Iavor: Estimating the effectiveness of permanent price reductions for competing products using multivariate Bayesian structural time series models (2022)
  3. Sahai, Saumya Yashmohini; Gurukar, Saket; KhudaBukhsh, Wasiur R.; Parthasarathy, Srinivasan; Rempała, Grzegorz A.: A machine learning model for nowcasting epidemic incidence (2022)
  4. Tanaka, Masahiro: Bayesian matrix completion approach to causal inference with panel data (2021)
  5. Wick, Felix; Kerzel, Ulrich; Hahn, Martin; Wolf, Moritz; Singhal, Trapti; Stemmer, Daniel; Ernst, Jakob; Feindt, Michael: Demand forecasting of individual probability density functions with machine learning (2021)
  6. Bühlmann, Peter: Invariance, causality and robustness (2020)
  7. Civantos, I.; García-Algarra, J.: Analysis of telecom service operation behavior with time series (2020)
  8. Feroze, Navid: Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian structural time series models (2020)
  9. Scharwächter, Erik; Müller, Emmanuel: Does terrorism trigger online hate speech? On the association of events and time series (2020)
  10. Bojinov, Iavor; Shephard, Neil: Time series experiments and causal estimands: exact randomization tests and trading (2019)
  11. Ning, Bo; Ghosal, Subhashis; Thomas, Jewell: Bayesian method for causal inference in spatially-correlated multivariate time series (2019)
  12. Samartsidis, Pantelis; Seaman, Shaun R.; Presanis, Anne M.; Hickman, Matthew; De Angelis, Daniela: Assessing the causal effect of binary interventions from observational panel data with few treated units (2019)
  13. Amjad, Muhammad; Shah, Devavrat; Shen, Dennis: Robust synthetic control (2018)
  14. Qiu, Jinwen; Jammalamadaka, S. Rao; Ning, Ning: Multivariate Bayesian structural time series model (2018)
  15. Schmitt, Eric; Tull, Christopher; Atwater, Patrick: Extending Bayesian structural time-series estimates of causal impact to many-household conservation initiatives (2018)
  16. Brodersen, Kay H.; Gallusser, Fabian; Koehler, Jim; Remy, Nicolas; Scott, Steven L.: Inferring causal impact using Bayesian structural time-series models (2015)