In podTockom/tstmle01: Targeted Maximum Likelihood Estimation of Causal Effects of Interventions on a Single Binary Time Series

R/`tstmle01`

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Estimation and Inference for Causal Effects with Single Binary Time Series

What's `tstmle01`?

The tstmle01 package implements targeted maximum likelihood estimation (TMLE) of the marginal causal effect based on the observation of a single binary time-series [@c1]. Current implementation supports iterative TMLE; check back for one-step and online TMLE [@c4], [@c5].

For the (faster) package implementing a more general methodology and other time-series based target parameters, see tstmle. In particular, tstmle implements the data-dependent, $C_{o(t)}-$ specific, causal effect and the adaptive design for learning the optimal treatment rule within a single time series [@c2]. Here, initial estimation is based on the sl3 package, which constructs ensemble models with proven optimality properties for time-series data [@c3].

We emphasize that this general formulation of the statistical estimation problem subsumes many other important estimation problems, including but not limited to classical time series models, group sequential adaptive designs, and even independent and identically distributed data when the summary measure of the past is simply the empty set.

Installation

You can install a stable release of tstmle01 from GitHub via devtools with:

devtools::install_github("podTockom/tstmle01")

Issues

If you encounter any bugs or have any specific feature requests, please file an issue.

Example

To illustrate how to ascertain the effect of an intervention on a single binary time series, consider the following example:

#Simulated data based on the simcausal package:
load(data)

#Estimate of the expected value of the outcome at time 5, under intervention on Anode 3:
res<-tstmle01(data,freqY=3,freqA=3,freqW=3,t=5,Anode=3,intervention1=1)
res$psi

License

The contents of this repository are distributed under the MIT license. See below for details:

The MIT License (MIT)

Copyright (c) 2017-2018

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