cde_did_aipw: Initialize an AIPW DID-CDE estimator

In mattblackwell/DirectEffects: Estimating Controlled Direct Effects for Explaining Causal Findings

Initialize an AIPW DID-CDE estimator

Description

Initializes the specification of a difference-in-differences estimator for the CDE based on an augmented inverse probability weighting.

Usage

cde_did_aipw(
  base_mediator,
  trim = c(0.01, 0.99),
  aipw_blip = TRUE,
  on_treated = FALSE
)

Arguments

base_mediator	The (unquoted) name of the variable that measures the mediator at baseline.
trim	A vector of length 2 indicating what quantiles of the propensity scores should be trimmed. By default this is c(0.01, 0.99) meaning that the top and bottom 1% of propensity scores are trunctated to these quantiles. If NULL, no trimming occurs.
aipw_blip	If TRUE (the default), augmented inverse probability weighting estimators will be used to estimate intermediate outcome regressions (blip functions).
on_treated	If FALSE (the defafult), the effects are average effects conditional on the levels of the baseline mediator. If TRUE, the effects are conditional on the treated path. For difference in identficiation, see Details below.

Details

This function, unlike other CDE estimators in the package, only returns the estimated effects of the first treatment variable. These effects are conditional on the baseline value of the mediator (base_mediator) when on_treated is TRUE. A marginalized CDE estimand is also estimated. When on_treated is FALSE, these estimates are conditional on the entire "treated" history. Identification requirements are slightly different between these two cases. When on_treated is FALSE, the confounders for the mediator cannot be affected by treatment. See Blackwell et al (2022) for more information.

mattblackwell/DirectEffects documentation built on Dec. 16, 2024, 6:14 p.m.