# mossspider Release 0.0.3

Targeted maximum likelihood estimation for network-dependent data

Keywords
tmle, network, causal-inference, network-analysis, targeted-maximum-likelihood
MIT
Install
``` pip install mossspider==0.0.3 ```

# MossSpider

MossSpider provides an implementation of the targeted maximum likelihood estimator for network-dependent data (network-TMLE) in Python. Currently `mossspider` supports estimation of the conditional network mean for stochastic policies.

`mossspider` get its name from the spruce-fir moss spider, a tarantula that is both the world's smallest tarantula and native to North Carolina.

## Installation

### Installing:

You can install via `python -m pip install mossspider`

### Dependencies:

The dependencies are: `numpy`, `scipy`, `statsmodels`, `networkx`, `matplotlib`. Notice that NetworkX must be at least 2.0.0 to work properly.

## Getting started

To demonstrate `mossspider`, below is a simple demonstration of calculating the mean for the following data.

```from mossspider import NetworkTMLE
from mossspider.dgm import uniform_network, generate_observed```

First, we will use some built-in data generating functions

```graph = uniform_network(n=500, degree=[1, 4])
graph_observed = generate_observed(graph)```

Now, we can use `NetworkTMLE` to estimate the causal conditional mean under a stochastic policy. Here, the stochastic policy sets everyone's probability of action `A=1` to 0.65.

```ntmle = NetworkTMLE(network=graph_observed,
exposure='A',  # Exposure in graph
outcome='Y',   # Outcome in graph
verbose=True)  # Print model summaries
ntmle.exposure_model(model="W + W_sum")
ntmle.exposure_map_model(model='A + W + W_sum',  # Parametric model
measure='sum',          # Summary measure for A^s
distribution='poisson') # Model distribution to use
ntmle.outcome_model(model='A + A_sum + W + W_sum')
ntmle.fit(p=0.65, samples=500)
ntmle.summary()```

For full details on using `mossspider`, see the full documentation and worked examples available at MossSpider website.