Controlling a manufacturing process using causal models

A system comprising:

• a memory configured to store data associated with an environment;
• and
• a processor in communication with the memory, the processor being configured to:
• cluster procedural instances based on one or more characteristics of the environment to form two or more clusters;
• identify each of the procedural instances as one of a hybrid instance or a baseline instance for a controllable element associated with the environment;
• select first control settings for the identified baseline instances;
• select second control settings for the identified hybrid instances;
• select treatment assignments for operation of a system in the environment based on a causal model mapped to a probability distribution over possible treatment assignments, wherein the causal model measures causal relationships between the treatment assignments and a performance metric associated with the environment, selection of the treatment assignments being for blocked groups according to a blocking scheme, the blocked groups formed based on a first blocked group including the baseline instances and a second blocked group including the hybrid instances;
• map causal effects between the treatment assignments and measures of uncertainty associated with the environment in the causal model to corresponding probabilities using probability matching;
• determine the performance metric using the treatment assignments and corresponding dependent variables obtained from the environment in response to the treatment assignments;
• adjust, based on the performance metric, the causal model;
• and
• re-compute the adjusted causal model by computing overall causal effects based on the measures of uncertainty around the overall causal effects.

A non-transitory computer-readable medium encoded with instructions that, when executed, cause a processor to:

• cluster procedural instances based on one or more characteristics of an environment to form two or more clusters;
• identify each of the procedural instances as one of a hybrid instance or a baseline instance for a controllable element associated with the environment;
• select first control settings for the identified baseline instances;
• select second control settings for the identified hybrid instances;
• select treatment assignments for operation of a system in the environment based on a causal model mapped to a probability distribution over possible treatment assignments, wherein the causal model measures causal relationships between the treatment assignments and a performance metric associated with the environment, selection of the treatment assignments being for blocked groups according to a blocking scheme, the blocked groups formed based on a first blocked group including the baseline instances and a second blocked group including the hybrid instances;
• map causal effects between the treatment assignments and measures of uncertainty associated with the environment in the causal model to corresponding probabilities using probability matching;
• determine the performance metric using the treatment assignments and corresponding dependent variables obtained from the environment in response to the treatment assignments;
• adjust, based on the performance metric, the causal model;
• and
• re-compute the adjusted causal model by computing overall causal effects based on the measures of uncertainty around the overall causal effects.