dosearch

Identification of causal effects from arbitrary observational and experimental probability distributions via do-calculus and standard probability manipulations using a search-based algorithm by Tikka, Hyttinen and Karvanen (2021) doi:10.18637/jss.v099.i05. Allows for the presence of mechanisms related to selection bias (Bareinboim and Tian, 2015) doi:10.1609/aaai.v29i1.9679, transportability (Bareinboim and Pearl, 2014) http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf, missing data (Mohan, Pearl, and Tian, 2013) http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf and arbitrary combinations of these. Also supports identification in the presence of context-specific independence (CSI) relations through labeled directed acyclic graphs (LDAG). For details on CSIs see (Corander et al., 2019) doi:10.1016/j.apal.2019.04.004.

Last 30 days

This package has been downloaded 385 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 14 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 5,864 times in the last 365 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The day with the most downloads was Jul 17, 2024 with 69 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.