Discovering causal relationships in mixed datasets
AssigneeAdobe Inc.
InventorsAyush Chauhan, Vineet Malik, Sourav Suman, Siddharth Jain, Gaurav Sinha, Aayush Makharia
Life of this patent
Estimating the term…
Abstract
Introduced here are approaches to determining causal relationships in mixed datasets containing data related to continuous variables and discrete variables. To accomplish this, a marketing insight and intelligence platform may employ a multi-phase approach in which dependency is established before the data related to continuous variables is discretized. Such an approach ensures that information regarding dependence is not lost through discretization.
Claims20
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.
A non-transitory computer-readable medium with instructions stored thereon that, when executed by a processor, cause the processor to perform operations 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 includes multiple nodes with edges connected therebetween;
- discretizing the data related to each continuous variable in the undirected graph to produce a discretized dataset from the mixed dataset;
- and
- generating a directed graph that reflects causal relationships among the multiple variables by:
- determining, for each of multiple candidate directed graphs, a metric that indicates how well a corresponding candidate directed graph fits the discretized dataset,
- identifying edges that are absent in the undirected graph, and
- selecting the directed graph from amongst the multiple candidate directed graphs based on (i) the multiple metrics and (ii) the identified edges.
A system comprising:
- at least one memory device;
- and
- at least one processor coupled to the at least one memory device that causes the system to perform operations comprising:
- producing an undirected graph that indicates dependency among variables in a mixed dataset by:
- computing dependency between the variables;
- and
- generating, prior to discretization, the undirected graph by adding, to a disconnected graph containing nodes that are representative of the variables, edges between nodes corresponding to variables that are not independent of one another;
- discretizing continuous data in a decreasing ratio based on a number of discrete variables neighboring each continuous variable in the undirected graph in the mixed dataset to produce a discretized dataset;
- and
- determining, based on the discretized dataset and the undirected graph, a directed graph that reflects causal relationships among the variables.
Dependencies are inferred from the claim text.