CausalImpact
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.
Keywords for this software
References in zbMATH (referenced in 16 articles )
Showing results 1 to 16 of 16.
Sorted by year (- Chen, Aiyou; Au, Timothy C.: Robust causal inference for incremental return on ad spend with randomized paired geo experiments (2022)
- Menchetti, Fiammetta; Bojinov, Iavor: Estimating the effectiveness of permanent price reductions for competing products using multivariate Bayesian structural time series models (2022)
- Sahai, Saumya Yashmohini; Gurukar, Saket; KhudaBukhsh, Wasiur R.; Parthasarathy, Srinivasan; Rempała, Grzegorz A.: A machine learning model for nowcasting epidemic incidence (2022)
- Tanaka, Masahiro: Bayesian matrix completion approach to causal inference with panel data (2021)
- 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)
- Bühlmann, Peter: Invariance, causality and robustness (2020)
- Civantos, I.; García-Algarra, J.: Analysis of telecom service operation behavior with time series (2020)
- 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)
- Scharwächter, Erik; Müller, Emmanuel: Does terrorism trigger online hate speech? On the association of events and time series (2020)
- Bojinov, Iavor; Shephard, Neil: Time series experiments and causal estimands: exact randomization tests and trading (2019)
- Ning, Bo; Ghosal, Subhashis; Thomas, Jewell: Bayesian method for causal inference in spatially-correlated multivariate time series (2019)
- 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)
- Amjad, Muhammad; Shah, Devavrat; Shen, Dennis: Robust synthetic control (2018)
- Qiu, Jinwen; Jammalamadaka, S. Rao; Ning, Ning: Multivariate Bayesian structural time series model (2018)
- Schmitt, Eric; Tull, Christopher; Atwater, Patrick: Extending Bayesian structural time-series estimates of causal impact to many-household conservation initiatives (2018)
- Brodersen, Kay H.; Gallusser, Fabian; Koehler, Jim; Remy, Nicolas; Scott, Steven L.: Inferring causal impact using Bayesian structural time-series models (2015)