sequential_module

Sequential neural network module function factory

Implements a modern, unified estimation strategy for common
mediation estimands (natural effects, organic effects, interventional effects,
and recanting twins) in combination with modified treatment policies as
described in Liu, Williams, Rudolph, and Díaz (2024)
<doi:10.48550/arXiv.2408.14620>. Estimation makes use of recent advancements
in Riesz-learning to estimate a set of required nuisance parameters with
deep learning. The result is the capability to estimate mediation effects with
binary, categorical, continuous, or multivariate exposures with
high-dimensional mediators and mediator-outcome confounders using machine
learning.

Nick Williams

crumble

Flexible and General Mediation Analysis Using Riesz Representers

Nicholas Williams

Richard Liu

Iván Díaz

sequential_module function

<dl><dt>layers</dt>
<dd>[numeric(1)] Number of hidden layers.</dd>
<dt>hidden</dt>
<dd>[numeric(1)] Number of hidden units.</dd>
<dt>dropout</dt>
<dd>[numeric(1)] Dropout rate.</dd></dl>

Arguments

Sequential neural network module function factory — sequential_module

<dl>

<dt>layers</dt>
<dd>[numeric(1)] Number of hidden layers.</dd>


<dt>hidden</dt>
<dd>[numeric(1)] Number of hidden units.</dd>


<dt>dropout</dt>
<dd>[numeric(1)] Dropout rate.</dd>

</dl>

sequential_module: Sequential neural network module function factory

Description

Usage

Value

Arguments

Examples