Causal influence, causal effects, and path analysis in the presence of intermediate confounding

idiazst/causal_influence 16 May 2022

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

TWEETS

Evaluating Forecasts with scoringutils in R

no code yet • 14 May 2022

Evaluating forecasts is essential in order to understand and improve forecasting and make forecasts useful to decision-makers.

Methodology Applications Computation

TWEETS

An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models

jtimonen/odemodeling 18 May 2022

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

TWEETS

BayesMix: Bayesian Mixture Models in C++

bayesmix-dev/bayesmix 17 May 2022

We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian mixture models.

Computation Other Statistics

TWEETS

High-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages

no code yet • 17 May 2022

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

TWEETS