US 12,008,589 B2
Discovering causal relationships in mixed datasets
Ayush Chauhan, Meerut (IN); Vineet Malik, Hisar (IN); Sourav Suman, Kendrapara (IN); Siddharth Jain, Kota (IN); Gaurav Sinha, Bangalore (IN); and Aayush Makharia, Surat (IN)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Jul. 31, 2023, as Appl. No. 18/362,833.
Application 18/362,833 is a continuation of application No. 17/097,508, filed on Nov. 13, 2020, granted, now 11,763,325.
Prior Publication US 2023/0385854 A1, Nov. 30, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/0201 (2023.01); G06F 16/901 (2019.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01)
CPC G06Q 30/0201 (2013.01) [G06F 16/9024 (2019.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
accessing a mixed dataset that contains data related to multiple variables, the multiple variables including at least one continuous variable and at least one discrete variable;
producing, prior to discretization, an undirected graph that indicates dependency among the multiple variables of the mixed dataset;
discretizing the data related to each continuous variable in a decreasing ratio based on a number of discrete variables neighboring each continuous variable in the undirected graph; and
identifying a directed graph that reflects causal relationships among the multiple variables by performing a greedy search of multiple candidate directed graphs using a scoring function that evaluates how well each candidate directed graph fits the discretized data.