US 2023/0144357 A1 · TREATMENT EFFECT ESTIMATION USING OBSERVATIONAL AND INTERVENTIONAL SAMPLES

Abstract

A treatment effect system estimates treatment effects by trading off between observational samples and interventional samples to maintain within a budget while providing high confidence. The treatment effect system determines whether to perform interventions by comparing the cost of interventional samples with metrics regarding the joint probability distribution of treatments and their parents in a first set of observational samples. If it is determined to not perform interventions, the treatment effect for each treatment is determined using an estimator that uses the first set of observational samples independent of a second set of observational samples. If it is determined to perform interventions, each treatment is identified as a reliable or unreliable treatment. The treatment effect for reliable treatments is estimated using an estimator that uses the first set of observational samples split into two portions. The treatment effect for unreliable treatments is estimated using interventional samples generated from interventions.

Claims20

Dependent claims

One or more computer storage media storing computer-useable instructions that, when used by a computing device, cause the computing device to perform operations, the operations comprising:

using a first set of observational samples to determine:
(1) a causal parameter that estimates skewness of a joint probability distribution of treatments and parents of the treatments in a causal graph, and (2) a minimum value of the joint probability distribution of the treatments and the parents of the treatments in the causal graph;
determining whether to perform interventions based on a comparison of a cost of interventional samples with the causal parameter and the minimum value;
based on a determination to not perform interventions, estimating a treatment effect for each treatment in the causal graph using the first set of observational samples and a second set of observational samples;
and
based on a determination to perform interventions:
identifying each treatment in the causal graph as either a reliable treatment or an unreliable treatment,
estimating a treatment effect for each reliable treatment using the first set of observational samples, and
estimating a treatment effect for each unreliable treatment using data collected from one or more interventions performed for each unreliable treatment.

6 more elements

A computerized method comprising:

determining, by an intervention module using a first set of observational samples, whether to use observational samples or use a combination of observational samples and interventional samples to estimate treatment effects for treatments in a causal graph;
based on a determination to use observational samples to estimate treatment effects, estimating, using an observational treatment effect module, a treatment effect for each treatment in the causal graph using the first set of observational samples and a second set of observational samples;
and
based on a determination to use a combination of observational samples and interventional samples to estimate treatment effects:
identifying, by a reliability module using the first set of observational samples, each treatment in the causal graph as either a reliable treatment or an unreliable treatment,
estimating, by the observational treatment effect module, a treatment effect for each reliable treatment using the first set of observational samples, and
estimating, by an interventional treatment effect module, a treatment effect for each unreliable treatment using one or more interventional samples generated based on one or more interventions performed for each unreliable treatment.

4 more elements

A computer system comprising:

a processor;
and
a computer storage medium storing computer-useable instructions that, when used by the processor, causes the computer system to perform operations comprising:
identifying, by a reliability module using a set of observational samples, whether each treatment from a causal graph is a reliable treatment or an unreliable treatment based on a comparison of:
(1) a causal parameter for each treatment that estimates skewness of a joint probability distribution of each treatment and parents of each treatment in the causal graph, and (2) a minimum value of the joint probability distribution of each treatment and the parents of each treatment in the causal graph;
estimating, by an observational treatment effect module, a treatment effect for each reliable treatment based on the set of observational samples;
estimating, by an interventional treatment effect module, a treatment effect for each unreliable treatment based on a set of interventional samples.

4 more elements

Dependencies are inferred from the claim text.

Treatment effect estimation using observational and interventional samples

Life of this patent

Treatment effect estimation using observational and interventional samples

Life of this patent

Abstract

Claims20