Recent approaches to causal inference have focused on the identification and estimation of \textit{causal effects}, defined as (properties of) the distribution of counterfactual outcomes under hypothetical actions that alter the nodes of a graphical model.
Methodology
Evaluating forecasts is essential in order to understand and improve forecasting and make forecasts useful to decision-makers.
Methodology Applications Computation
Statistical models can involve implicitly defined quantities, such as solutions to nonlinear ordinary differential equations (ODEs), that unavoidably need to be numerically approximated in order to evaluate the model.
Computation
We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian mixture models.
Computation Other Statistics
Estimating forest aboveground biomass at fine spatial scales has become increasingly important for greenhouse gas estimation, monitoring, and verification efforts to mitigate climate change.
Applications