*! version 1.2.1 07mar2013

Title

tesensitivity -- Sensitivity analysis for treatment effect models based on unconfoundedness

Syntax

tesensitivity subcommand ... [, options]

subcommand

cpi conditional partial independence analysis

cscale c-dependence scale analysis

cpiplot conditional partial independence analysis plot

cpitable conditional partial independence analysis table

Description

tesensitivity analyzes the sensitivity of treatment effect estimates to relaxations of the unconfoundedness assumption, also known as selection on observables, exogenous selection, ignorability, or conditional independence.
tesensitvity cpi calculates bounds on treatment effect parameters by relaxing the unconfoundedness assumption to a conditional partial independence assumption.
Bounds are calculated for a range of alternative assumptions about the magnitude of selection on unobservables.
The breakdown point for conclusions about these treatment effects is also calculated.
This is defined as the maximum level of selection on unobservables under which a specific conclusion still holds.

Conditional partial independence analysis can be conducted for five treatment
effect parameters: the average treatment effect (ATE), the average treatment effect on the treated (ATET), quantile treatment effects (QTE), conditional average treatment effects (CATE), and conditional quantile treatment effects (CQTE).

tesensitivity cpiplot, tesensitivity ctable, and tesensitivity cscale
provide tools to visualize the results, compare results from multiple datasets, and interpret the the scale of c-dependence.

Further Information

This package implements the sensitivity analysis described in Masten and Poirier (2018)
(Preprint) and
Masten, Poirier, and Zhang (2020).
Masten and Poirier (2020) discuss the concept of a breakdown point.

This vignette
provides a tutorial for use of this package walking through the empirical illustration in
Masten, Poirier, and Zhang (2020)
using data from Dehejia and Wahba (1999)
on a program that provided guaranteed work experience, which is reconstructed from LaLonde (1986). If you are new to this package, this vignette is the best place to start.

References

Dehejia and Wahba (1999) Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs, The Journal of the American Statistical Association

LaLonde (1986) Evaluating the Econometric Evaluations of Training Programs with Experimental Data, The American Economic Review

Masten and Poirier (2018) Identification of Treatment Effects under Conditional Partial Independence, Econometrica

Masten and Poirier (2020) Inference on Breakdown Frontiers, Quantitative Economics

Masten, Poirier, and Zhang (2020) Assessing Sensitivity to Unconfoundedness: Estimation and Inference, arXiv preprint