# tmlenet v0.1.0

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## Targeted Maximum Likelihood Estimation for Network Data

Estimation of average causal effects for single time point interventions in network-dependent data (e.g., in the presence of spillover and/or interference). Supports arbitrary interventions (static or stochastic). Implemented estimation algorithms are the targeted maximum likelihood estimation (TMLE), the inverse-probability-of-treatment (IPTW) estimator and the parametric G-computation formula estimator. Asymptotically correct influence-curve-based confidence intervals are constructed for the TMLE and IPTW. The data are assumed to consist of rows of unit-specific observations, each row i represented by variables (F.i,W.i,A.i,Y.i), where F.i is a vector of friend IDs of unit i (i's network), W.i is a vector of i's baseline covariates, A.i is i's exposure (can be binary, categorical or continuous) and Y.i is i's binary outcome. Exposure A.i depends on (multivariate) user-specified baseline summary measure(s) sW.i, where sW.i is any function of i's baseline covariates W.i and the baseline covariates of i's friends in F.i. Outcome Y.i depends on sW.i and (multivariate) user-specified summary measure(s) sA.i, where sA.i is any function of i's baseline covariates and exposure (W.i,A.i) and the baseline covariates and exposures of i's friends. The summary measures are defined with functions def.sW and def.sA. See ?'tmlenet-package' for a general overview.

# tmlenet

The tmlenet R package performs estimation of average causal effects for single time point interventions in network-dependent (non-IID) data in the presence of interference and/or spillover. Currently implemented estimation algorithms are the targeted maximum likelihood estimation (TMLE), Horvitz-Thompson or the inverse-probability-of-treatment (IPTW) estimator and the parametric G-computation estimator. The user-specified interventions can be either static, dynamic or stochastic. Asymptotically correct influence-curve-based confidence intervals are also constructed for the TMLE and IPTW. See the paper below for more information on the estimation methodology employed by the tmlenet R package:

M. J. van der Laan, “Causal inference for a population of causally connected units,” J. Causal Inference J. Causal Infer., vol. 2, no. 1, pp. 13–74, 2014.

### Installation

To install the development version of tmlenet (requires the devtools package):

devtools::install_github('osofr/tmlenet', build_vignettes = FALSE)


### Documentation

Once the package is installed, please refer to the help file ?'tmlenet-package' and tmlenet function documentation for details and examples:

?'tmlenet-package'
?tmlenet


### The input data and the network summary measures

The input data are assumed to consist of rows of unit-specific observations, with each row i represented by variables (F.i,W.i,A.i,Y.i), where F.i is a vector of "friend IDs" of unit i (also referred to as i's "network"), W.i is a vector of i's baseline covariates, A.i is i's exposure (either binary, categorical or continuous) and Y.i is i's binary outcome.

Each exposure A.i depends on (possibly multivariate) baseline summary measure(s) sW.i, where sW.i can be any user-specified function of i's baseline covariates W.i and the baseline covariates of i's friends in F.i (all W.j such that j is in F.i). Similarly, each outcome Y.i depends on sW.i and (possibly multivariate) summary measure(s) sA.i, where sA.i can be any user-specified function of i's baseline covariates and exposure (W.i,A.i) and the baseline covariates and exposures of i's friends (all W.j,A.j such that j is in i's friend set F.i).

The summary measures (sW.i,sA.i) are defined simultaneously for all i with functions def.sW and def.sA. It is assumed that (sW.i,sA.i) have the same dimensionality across i. The function eval.summaries can be used for evaluating these summary measures.

All estimation is performed by calling the tmlenet function. The vector of friends F.i can be specified either as a single column in the input data (where each F.i is a string of friend IDs or friend row numbers delimited by character sep) or as a separate input matrix of network IDs (where each row is a vector of friend IDs or friend row numbers). Specifying the network as a matrix generally results in significant improvements to run time. See tmlenet function help file for additional details on how to specify these and the rest of the input arguments.

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### Citation

To cite tmlenet in publications, please use:

Sofrygin O, van der Laan MJ (2015). tmlenet: Targeted Maximum Likelihood Estimation for Networks. R package version 0.1.

### Funding

The development of this package was funded through an NIH grant (R01 AI074345-07).

## Functions in tmlenet

 Name Description RegressionClass R6 class that defines regression models evaluating P(sA|sW), for summary measures (sW,sA) df_netKmax6 An example of a row-dependent dataset with known network of at most 6 friends. DatNet R6 class for storing and managing already evaluated summary measures sW or sA (but not both at the same time). CategorSummaryModel R6 class for fitting and predicting joint probability for a univariate categorical summary measure sA[j] print_tmlenet_opts Print Current Option Settings for tmlenet eval.summaries Evaluate Summary Measures sA and sW tmlenet_options Setting Options for tmlenet NetInd_mat_Kmax6 An example of a network ID matrix SummariesModel R6 class for fitting and predicting model P(sA|sW) under g.star or g.0 tmlenet-package Targeted Maximum Likelihood Estimation for Network Data mcEvalPsi R6 class for Monte-Carlo evaluation of various substitution estimators for exposures generated under the user-specified stochastic intervention function. BinDat R6 class for storing the design matrix and binary outcome for a single logistic regression DatNet.sWsA R6 class for storing and managing the combined summary measures sW & sA from DatNet classes. tmlenet Estimate Average Network Effects For Arbitrary (Stochastic) Interventions DefineSummariesClass R6 class for parsing and evaluating user-specified summary measures (in exprs_list) def.sW Define Summary Measures sA and sW Define_sVar R6 class for parsing and evaluating node R expressions. df_netKmax2 An example of a row-dependent dataset with known network of at most 2 friends. BinOutModel R6 class for fitting and making predictions for a single logistic regression with binary outcome B, P(B | PredVars) ContinSummaryModel R6 class for fitting and predicting joint probability for a univariate continuous summary measure sA[j] No Results!