toydata

<p>A dataset containing 4 columns: <code>y</code> for a continuous outcome,
<code>d</code> for a binary treatment, <code>x</code> for a pretreatment covariate,
and <code>z</code> for an excluded instrument.</p>

datasets

In the generalized Roy model, the marginal treatment effect (MTE) can be used as
a building block for constructing conventional causal parameters such as the average treatment
effect (ATE) and the average treatment effect on the treated (ATT). Given a treatment selection
equation and an outcome equation, the function mte() estimates the MTE via the semiparametric
local instrumental variables method or the normal selection model. The function mte_at() evaluates
MTE at different values of the latent resistance u with a given X = x, and the function mte_tilde_at()
evaluates MTE projected onto the estimated propensity score. The function ace() estimates
population-level average causal effects such as ATE, ATT, or the marginal policy relevant
treatment effect.

Xiang Zhou

localIV

Estimation of Marginal Treatment Effects using Local
Instrumental Variables

toydata function

<p>An object of class <code>data.frame</code> with 10000 rows and 4 columns.</p>

toydata: A Hypothetical Dataset for Illustrative Purpose

Description

Usage

Arguments

Format