Methods and apparatus to determine a causal effect of observation data without reference data

Patent metadata

Field	Value
Publication number	US-2017193529-A1
Application number	US-201615046052-A
Country	US
Kind code	A1
Filing date	Feb 17, 2016
Priority date	Dec 31, 2015
Publication date	Jul 6, 2017
Grant date	—

Abstract

Methods, apparatus, systems and articles of manufacture are disclosed to determine a causal effect of observation data without reference data. An example method includes retrieving, by executing an instruction with a processor, observation data without associated reference data, eliminating a need for the processor to randomize reference data to reduce error by generating, with the processor, mutually exclusive categories of interest of the observation data, associating, by executing an instruction with the processor, each category of interest with a respective control group and treatment group; and for each iteration of a bootstrap: selecting, by executing an instruction with the processor, a random subgroup of the observation data, constraining, by executing an instruction with the processor, respective proportions of the control group and the treatment group to converge to a substantially equal value, solving for weight values of the mutually exclusive categories of interest based on the constrained proportions of the control group and the treatment group by executing an instruction with the processor, and generating, with the processor, a causal effect estimate value based on the weight values.

First claim

What is claimed is:

1 . A computer-implemented method, comprising:
retrieving, by executing an instruction with a processor, observation data without associated reference data; eliminating a need for the processor to randomize reference data to reduce error by generating, with the processor, mutually exclusive categories of interest of the observation data; associating, by executing an instruction with the processor, each category of interest with a respective control group and treatment group; and for each iteration of a bootstrap:
selecting, by executing an instruction with the processor, a random subgroup of the observation data;
constraining, by executing an instruction with the processor, respective proportions of the control group and the treatment group to converge to a substantially equal value;
solving for weight values of the mutually exclusive categories of interest based on the constrained proportions of the control group and the treatment group by executing an instruction with the processor; and
generating, with the processor, a causal effect estimate value based on the weight values.

2 . The method as defined in claim 1 , further including generating a histogram of respective iterations of the bootstrap to reveal a causal effect of the stimulus on at least one of the mutually exclusive categories of interest.

3 . The method as defined in claim 1 , wherein the generating of the causal effect estimate value includes calculating a naïve estimate difference value between the control group and the treatment group.

4 . The method as defined in claim 1 , further including calculating raw proportion values for the control group and the treatment group from the random subgroup of observation data.

5 . The method as defined in claim 4 , further including calculating an optimized proportion value for the control group and the treatment group based on the weight values and the constraint to converge to an equal value.

6 . The method as defined in claim 1 , wherein the observation data is indicative of participants that (a) have been exposed to a stimulus and (b) have not been exposed to the stimulus.

7 . The method as defined in claim 6 , wherein the control group is indicative of a first portion of the observation data not associated with the stimulus and the treatment group is indicative of a second portion of the observation data associated with the stimulus.

8 . An apparatus, comprising:
an observation data interface to retrieve observation data without associated reference data; a data category generator to eliminate a need to randomize reference data to reduce error by generating mutually exclusive categories of interest of the observation data; a control/treatment group generator to associate each category of interest with a respective control group and treatment group; a bootstrap engine to select a random subgroup of the observation data for each iteration of a bootstrap; a constraint engine to constrain respective proportions of the control group and the treatment group to converge to a substantially equal value for each iteration of the bootstrap; a weighting engine to solve for weight values of the mutually exclusive categories of interest based on the constrained proportions of the control group and the treatment group for each iteration of the bootstrap; and an output engine to generate a causal effect estimate value based on the weight values for each iteration of the bootstrap.

9 . The apparatus as defined in claim 8 , wherein the output engine is to generate a histogram of respective iterations of the bootstrap to reveal a causal effect of the stimulus on at least one of the mutually exclusive categories of interest.

10 . The apparatus as defined in claim 8 , wherein the bootstrap engine is to calculate a naïve estimate difference value between the control group and the treatment group.

11 . The apparatus as defined in claim 8 , further including a population engine is to calculate raw proportion values for the control group and the treatment group from the random subgroup of observation data.

12 . The apparatus as defined in claim 11 , wherein the constraint engine is to calculate an optimized proportion value for the control group and the treatment group based on the weight values and the constraint to converge to an equal value.

13 . The apparatus as defined in claim 8 , wherein the observation data is indicative of participants that (a) have been exposed to a stimulus and (b) have not been exposed to the stimulus.

14 . The apparatus as defined in claim 13 , wherein the control group is indicative of a first portion of the observation data not associated with the stimulus and the treatment group is indicative of a second portion of the observation data associated with the stimulus.

15 . A tangible computer readable storage medium comprising instructions that, when executed, cause a processor to, at least:
retrieve observation data without associated reference data; eliminate a need for the processor to randomize reference data to reduce error by generating mutually exclusive categories of interest of the observation data; associate each category of interest with a respective control group and treatment group; and for each iteration of a bootstrap:
select a random subgroup of the observation data;
constrain respective proportions of the control group and the treatment group to converge to a substantially equal value;
solve for weight values of the mutually exclusive categories of interest based on the constrained proportions of the control group and the treatment group by executing an instruction with the processor; and
generate a causal effect estimate value based on the weight values.

16 . The machine readable instructions as defined in claim 15 , wherein the instructions, when executed, cause the processor to generate a histogram of respective iterations of the bootstrap to reveal a causal effect of the stimulus on at least one of the mutually exclusive categories of interest.

17 . The machine readable instructions as defined in claim 15 , wherein the instructions, when executed, cause the processor to calculate a naïve estimate difference value between the control group and the treatment group.

18 . The machine readable instructions as defined in claim 15 , wherein the instructions, when executed, cause the processor to calculate raw proportion values for the control group and the treatment group from the random subgroup of observation data.

19 . The machine readable instructions as defined in claim 18 , wherein the instructions, when executed, cause the processor to calculate an optimized proportion value for the control group and the treatment group based on the weight values and the constraint to converge to an equal value.

20 . The machine readable instructions as defined in claim 15 , wherein the instructions, when executed, cause the processor to employ observation data that is indicative of participants that (a) have been exposed to a stimulus and (b) have not been exposed to the stimulus.

Assignees

• Nielsen Co Us Llc

Inventors

• Sheppard Michael
• Sullivan Jonathan
• Lipa Peter
• Terrazas Alejandro
• Torres John Charles

Classifications

• G06Q30/0201 Primary

  Market modelling; Market analysis; Collecting market data · CPC title

• G06Q30/0245

  Surveys · CPC title