Combining counterfactual outcomes and ARIMA models for policy evaluation
Summary The Rubin Causal Model (RCM) is a framework that allows to define the causal
effect of an intervention as a contrast of potential outcomes. In recent years, several methods …
effect of an intervention as a contrast of potential outcomes. In recent years, several methods …
Spatial causal inference in the presence of unmeasured confounding and interference
G Papadogeorgou, S Samanta - arXiv preprint arXiv:2303.08218, 2023 - arxiv.org
This manuscript bridges the divide between causal inference and spatial statistics,
presenting novel insights for causal inference in spatial data analysis, and establishing how …
presenting novel insights for causal inference in spatial data analysis, and establishing how …
Multivariate Bayesian dynamic modeling for causal prediction
Bayesian forecasting is developed in multivariate time series analysis for causal inference.
Causal evaluation of sequentially observed time series data from control and treated units …
Causal evaluation of sequentially observed time series data from control and treated units …
Effects of New York City's neighborhood policing policy
B Beck, J Antonelli, G Piñeros - Police Quarterly, 2022 - journals.sagepub.com
Between 2015 and 2018, New York City adopted “neighborhood policing,” an expansive
policy to encourage interactions between police officers and community members. Among …
policy to encourage interactions between police officers and community members. Among …
Estimating the effects of a California gun control program with multitask Gaussian processes
Estimating the effects of a California gun control program with multitask Gaussian processes
Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 2, 985–1016 https://doi.org/10.1214/22-AOAS1654 …
Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 2, 985–1016 https://doi.org/10.1214/22-AOAS1654 …
Dynamic graphical models: Theory, structure and counterfactual forecasting
M West, L Vrotsos - arXiv preprint arXiv:2410.06125, 2024 - arxiv.org
Simultaneous graphical dynamic linear models (SGDLMs) provide advances in flexibility,
parsimony and scalability of multivariate time series analysis, with proven utility in …
parsimony and scalability of multivariate time series analysis, with proven utility in …
Compositional Dynamic Modelling for Counterfactual Prediction in Multivariate Time Series
Theoretical developments in sequential Bayesian analysis of multivariate dynamic models
underlie new methodology for counterfactual prediction. This extends the utility of existing …
underlie new methodology for counterfactual prediction. This extends the utility of existing …
[图书][B] Losing Control (Group)? The Machine Learning Control Method for Counterfactual Forecasting
The standard way of estimating treatment effects relies on the availability of a similar group
of untreated units. Without it, the most widespread counterfactual methodologies cannot be …
of untreated units. Without it, the most widespread counterfactual methodologies cannot be …
Compositional dynamic modelling for causal prediction in multivariate time series
Theoretical developments in sequential Bayesian analysis of multivariate dynamic models
underlie new methodology for causal prediction. This extends the utility of existing models …
underlie new methodology for causal prediction. This extends the utility of existing models …
Robust inference for geographic regression discontinuity designs: assessing the impact of police precincts
We study variation in policing outcomes attributable to differential policing practices in New
York City (NYC) using geographic regression discontinuity designs. By focusing on small …
York City (NYC) using geographic regression discontinuity designs. By focusing on small …