# iv_finite_factorial: Finite-Sample IV Estimation of 2^K Factorial Design In mattblackwell/factiv: Instrumental Variables Estimation for 2^k Factorial Experiments

## Description

Estimate main effect IV ratios for 2^K factorial experiments

## Usage

 `1` ```iv_finite_factorial(formula, data, subset, level = 0.95) ```

## Arguments

 `formula` formula specification of the factorial design with noncompliance. The right-hand side of the formula should have two components separated by the `|` symbol, with the first component containing the K binary treatment variables and the second component containing the K binary instruments associated with each treatment variable. The order of the variables in the formula must match. `data` a data.frame on which to apply the `formula`. `subset` subset of the data to pass to estimation. `level` the confidence level required.

## Details

This function estimates the ratio of the effect of treatment assignment on the outcome to the effect of treatment assignment on treatment uptake in 2^K factorial experiments. The approach uses finite sample asymptotic inference to generate confidence intervals.

## Value

A list of class `iv_finite_factorial` that contains the following components:

 `tau` a vector of estimated effect ratios for each factor. `tau_cis` a matrix of confidence intervals for each effect ratio. This matrix has 4 columns because it is possible to have disjoint confidence intervals in this method. `tau_y` a vector of the estimated effects on the outcome. `v_tau_y` the estimated sample variances of the effects on the outcome. `tau_d` a vector of the estimated effects on treatment uptake. `v_tau_y` the estimated sample variances of the effects on treatment uptake. `level` the confidence level of `tau_cis`.

Matt Blackwell

## References

Matthew Blackwell and Nicole Pashley (2021) "Noncompliance in Factorial Experiments." Working paper.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```data(newhaven) out <- iv_finite_factorial(turnout_98 ~ inperson + phone | inperson_rand + phone_rand, data = newhaven) out joint <- iv_finite_factorial(turnout_98 ~ inperson + phone | inperson_rand + phone_rand, data = newhaven) joint ```

mattblackwell/factiv documentation built on Dec. 13, 2021, 5:49 p.m.