netchain-package: netchain: causal inference on collective outcomes

In youjin1207/netchain: Inferring Causal Effects on Collective Outcomes under Interference

Description

This package is for estimation of probability associated with collective counterfactual outcomes using approximation via causal graphical model. We apply a parsimonious parameterization for social network data with some specific kinds of interference and contagion, which corresponds to particular family of graphical models known as chain graphs.

Details

We provide functions to estimate the parameters in conditional log-linear model when the observations (outcomes, treatments, and confounders) and the structure of a causal graph is given. Based on the estimated parameters, we generate counterfactual outcomes using Gibbs sampling to infer the causal effect (or causal probability) of a certain treatment assignment on the collective outcomes. Moreover, we use this method to identify causally influential units on social network.

Author(s)

Youjin Lee

Maintainer: Youjin Lee <[e-mail]>

See Also

https://github.com/youjin1207/netchain

youjin1207/netchain documentation built on May 7, 2019, 9:32 a.m.