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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# imtp
<!-- badges: start -->
[](https://www.repostatus.org/#wip)
<!-- badges: end -->
> Non-Parametric Causal Effects Based on Incremental Propensity Score Interventions
An implementation of the incremental propensity score intervention estimator described in [Kennedy (2019)](https://doi.org/10.1080/01621459.2017.1422737). The UI is implemented in the same manner as the [`lmtp`](https://github.com/nt-williams/lmtp) package and provides a compliment to the main objective of [`lmtp`](https://github.com/nt-williams/lmtp) for when treatment/exposure is binary.
#### Are incremental propensity score interventions MTPs?
Yes! A modified treatment policy is simply an intervention that can be written as a function of the natural value of exposure. Using this defintion, an incremenental propensity score intervention may be defined as a modified treatment policy. See Example 3 in [*Non-parametric causal effects based on longitudinal modified treatment policies*](https://doi.org/10.1080/01621459.2021.1955691) for more details.
## Installation
You can install the development version of `imtp` from [GitHub](https://github.com/) with:
``` r
devtools::install_github("mtpverse/imtp")
```
## Example
```{r example, eval=FALSE}
library(imtp)
n <- 1000
W <- matrix(rnorm(n*3), ncol = 3)
A <- rbinom(n, 1, 1/(1 + exp(-(.2*W[,1] - .1*W[,2] + .4*W[,3]))))
Y <- A + 2*W[,1] + W[,3] + W[,2]^2 + rnorm(n)
R <- rbinom(n, 1, 0.9)
tmp <- data.frame(W, A, R, Y = ifelse(R == 1, Y, NA_real_))
imtp_tmle(tmp, "A", "Y", paste0("X", 1:3), cens = "R", delta = 2, outcome_type = "continuous")
#> IPSI Estimator: TMLE
#> delta: 2
#>
#> Population intervention estimate
#> Estimate: 1.6243
#> Std. error: 0.107
#> 95% CI: (1.4145, 1.834)
deltas <- seq(0.1, 2, length.out = 5)
fits <- lapply(deltas, function(d) imtp_tmle(tmp, "A", "Y", paste0("X", 1:3), cens = "R", delta = d, outcome_type = "continuous"))
imtp_simul(fits)
#> theta mult.conf.low mult.conf.high
#> 1 1.064633 0.9057856 1.223480
#> 2 1.313744 1.1279433 1.499544
#> 3 1.461470 1.2513807 1.671560
#> 4 1.569143 1.3432812 1.795005
#> 5 1.630143 1.3904212 1.869865
```
## References
Edward H. Kennedy (2019) Nonparametric Causal Effects Based on Incremental Propensity Score Interventions, Journal of the American Statistical Association, 114:526, 645-656, DOI: 10.1080/01621459.2017.1422737
Kwangho Kim and Edward H. Kennedy and Ashley I. Naimi (2019) Incremental Intervention Effects in Studies with Many Timepoints, Repeated Outcomes, and Dropout, arXiv: 1907.04004
Iván Díaz, Nicholas Williams, Katherine L. Hoffman & Edward J. Schenck (2021) Non-parametric causal effects based on longitudinal modified treatment policies, Journal of the American Statistical Association, DOI: 10.1080/01621459.2021.1955691