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dosearch

dosearch: R Package for Identifying General Causal Queries

https://github.com/santikka/dosearch

Science Score: 39.0%

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    Found 9 DOI reference(s) in README
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Keywords

c-plus-plus causal-inference causal-models causality causality-algorithms directed-acyclic-graph graphs labeled-graphs r
Last synced: 6 months ago · JSON representation

Repository

dosearch: R Package for Identifying General Causal Queries

Basic Info
  • Host: GitHub
  • Owner: santikka
  • License: gpl-3.0
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 2.64 MB
Statistics
  • Stars: 8
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Topics
c-plus-plus causal-inference causal-models causality causality-algorithms directed-acyclic-graph graphs labeled-graphs r
Created about 5 years ago · Last pushed 7 months ago
Metadata Files
Readme Changelog Contributing License Codemeta

README.Rmd 

---
output: github_document
---



```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r srr-tags, eval = FALSE, echo = FALSE}
#' @srrstats {G1.0} Primary reference is:
#'   S. Tikka, A. Hyttinen and J. Karvanen.
#'   "Causal effect identification from multiple incomplete data sources:
#'   a general search-based approach." \emph{Journal of Statistical Software},
#'   99(5):1--40, 2021.
#' @srrstats {G1.1} First implementation of an original algorithm.
#' @srrstats {G1.2} Life Cycle Statement is included in the README.
```

# dosearch


[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![R-CMD-check](https://github.com/santikka/dosearch/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/santikka/dosearch/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/gh/santikka/dosearch/branch/master/graph/badge.svg)](https://app.codecov.io/gh/santikka/dosearch?branch=master)
[![CRAN version](https://www.r-pkg.org/badges/version/dosearch)](https://CRAN.R-project.org/package=dosearch)



The `dosearch` [R](https://www.r-project.org/) package facilitates 
identification of causal effects from arbitrary observational and experimental 
probability distributions via do-calculus and standard probability 
manipulations using a search-based algorithm (Tikka et al., 2019, 2021).
Formulas of identifiable target distributions are returned in character format
using LaTeX syntax. The causal graph may additionally include mechanisms 
related to: 

* Selection bias (Bareinboim and Tian, 2015)
* Transportability (Bareinboim and Pearl, 2014) 
* Missing data (Mohan et al., 2013)
* Context-specific independence (Corander et al., 2019)
  
See the package vignette or the references for further information.
  
### Citing the package

If you use the `dosearch` package in a publication, please cite the 
corresponding paper in the Journal of Statistical Software:

Tikka S, Hyttinen A, Karvanen J (2021). “Causal Effect Identification from 
Multiple Incomplete Data Sources: A General Search-Based Approach.” 
*Journal of Statistical Software*, 99(5), 1--40. 
[doi:10.18637/jss.v099.i05](https://doi.org/10.18637/jss.v099.i05).

## Installation

You can install the latest release version from CRAN:
```{r, eval = FALSE}
install.packages("dosearch")
```

Alternatively, you can install the latest development version of `dosearch`:
```{r, eval = FALSE}
# install.packages("devtools")
devtools::install_github("santikka/dosearch")
```

## Examples

```{r, echo = FALSE}
library(dosearch)
```

```{r, eval = TRUE}
# back-door formula
data <- "p(x,y,z)"
query <- "p(y|do(x))"
graph <- "
  x -> y
  z -> x
  z -> y
"
dosearch(data, query, graph)

# front-door formula
graph <- "
  x -> z
  z -> y
  x <-> y
"
dosearch(data, query, graph)

# the 'napkin' graph
data <- "p(x,y,z,w)"
graph <- "
  x -> y
  z -> x
  w -> z
  x <-> w
  w <-> y
"
dosearch(data, query, graph)

# case-control design
data <- "
  p(x*,y*,r_x,r_y)
  p(y)
"
graph <- "
  x -> y
  y -> r_y
  r_y -> r_x
"
md <- "r_x : x, r_y : y"
dosearch(data, query, graph, missing_data = md)
```

## References

* Tikka S, Hyttinen A, Karvanen J (2021).
  "Causal effect identification from multiple incomplete data sources: a general search-based approach." 
  *Journal of Statistical Software*, 
  99(5), 1--40.
  [doi:10.18637/jss.v099.i05](https://doi.org/10.18637/jss.v099.i05)
* Tikka S, Hyttinen A, Karvanen J (2019). 
  "Identifying causal effects via context-specific independence relations." 
  In *Proceedings of the 33rd Annual Conference on Neural Information Processing Systems*.
  (https://papers.nips.cc/paper/2019/hash/d88518acbcc3d08d1f18da62f9bb26ec-Abstract.html)
* Bareinboim E, Tian J (2015).
  "Recovering causal effects from selection bias." 
  In *Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence*,
  (http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf)
* Bareinboim E, Pearl J (2014).
  "Transportability from multiple environments with limited Experiments: completeness Results." 
  In *Advances of Neural Information Processing Systems 27*,
  (http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf)
* Mohan K, Pearl J, Tian J (2013).
  "Graphical models for inference with missing data." 
  In *Advances of Neural Information Processing Systems 26*,
  (http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf)
* Corander J, Hyttinen A, Kontinen J, Pensar J, Väänänen J (2019).
  "A logical approach to context-specific independence." 
  *Annals of Pure and Applied Logic*,
  170(9), 975--992. 
  [doi:10.1016/j.apal.2019.04.004](https://doi.org/10.1016/j.apal.2019.04.004)

Owner

  • Name: Santtu Tikka
  • Login: santikka
  • Kind: user
  • Location: Finland
  • Company: University of Jyväskylä

Postdoctoral researcher at University of Jyväskylä, Department of Mathematics and Statistics.

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cran.r-project.org: dosearch

Causal Effect Identification from Multiple Incomplete Data Sources

  • Versions: 10
  • Dependent Packages: 1
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santtuth@gmail.com
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