*! version 30aug2024

help ddml overlap v1.4.4

ddml overlap commands for Double Debiased Machine Learning

ddml implements algorithms for causal inference aided by supervised
machine learning as proposed in
Double/debiased machine learning for treatment and structural parameters
(Econometrics Journal, 2018). Five different models are supported, allowing for
binary or continuous treatment variables and endogeneity, high-dimensional
controls and/or instrumental variables.

ddml overlap reports overlap plots following estimation of the ddml { interactive and interactiveiv models.
One overlap (line) plot of propensity scores is reported for each treatment variable learner;
by default, propensity scores for all crossfit samples are plotted.
Overlap plots for the treatment variables are combined using graph combine.

Syntax

ddml overlap [ mname(name) replist(numlist) pslist(namelist) n(integer) kernel(name)
name(name [, replace]) title(string) subtitle(string) lopt0(string)
lopt1(string) ]

Options

mname(name)
name of the DDML model. Defaults to m0.

replist(numlist)
list of crossfitting resamples to plot. Defaults to all.

pslist(namelist)
varnames of propensity scores to plot (excluding the resample number). Defaults to all.

n(integer)
see teffects overlap.

kernel(name)
see teffects overlap.

name(name)
see graph combine.

title(string)
see graph combine.

subtitle(string)
see graph combine.

lopt0(string)
options for line plot of untreated; default is solid/navy; see line.

lopt0(string)
options for line plot of treated; default is short dash/dark orange; see line.

Examples