Causal Inference Machine Learning with Statistical Background Subtraction

Patent metadata

Field	Value
Publication number	US-2023419184-A1
Application number	US-202318367914-A
Country	US
Kind code	A1
Filing date	Sep 13, 2023
Priority date	Jan 13, 2020
Publication date	Dec 28, 2023
Grant date	—

Abstract

A system and method are disclosed to generate causal inference machine learning models employing statistical background subtraction. Embodiments include a server comprising a processor and memory. Embodiments receive historical sales data for one or more past time periods and corresponding historical data for one or more causal variables. Embodiments deconfound the cause-effect relationship of historical sales data and historical data on the one or more causal variables. Embodiments define one or more sample weights for statistical background subtraction of the historical data and perform statistical background subtraction on the historical data. Embodiments train a first machine learning model to predict an absolute individual causal effect on a considered demand quantity in relation to the one or more causal variables and one or more sample weights.

First claim

What is claimed is:

1 . A computer-implemented method, comprising:
receiving, with a server comprising a processor and memory, historical sales data for one or more past time periods and corresponding historical data for one or more causal variables; deconfounding, by the server, a cause-effect relationship of historical sales data and historical data on the one or more causal variables by conducting one or more randomized controlled A/B group trials; defining, by the server, one or more sample weights for statistical background subtraction of the historical data; performing, by the server, statistical background subtraction on the historical data; training, by the server, by an iterative approach comprising cyclic boosting in additive regression mode, a first machine learning model to predict an absolute individual causal effect on a considered demand quantity; and predicting, by the server, with the first machine learning model, an absolute individual causal effect on one or more considered demand quantities during a prediction period by training a second machine learning model.

2 . The computer-implemented method of claim 1 , wherein a casual variable comprises a sending of a personalized coupon.

3 . The computer-implemented method of claim 1 , further comprising:
restricting, by the server, a learning of a causal factor between a feature describing seasonality over a year and a target to a smooth sinusoidal dependency.

4 . The computer-implemented method of claim 1 , further comprising:
assigning, by the server, one or more positive sample weights and one or more negative sample weights to one or more samples of the one or more randomized controlled A/B group trials.

5 . The computer-implemented method of claim 1 , further comprising:
predicting, by the server, one or more what-if volume predictions from one or more sets of hypothetical causal factors.

6 . The computer-implemented method of claim 1 , further comprising:
predicting, by the server, one or more individual causal effects on gross margin.

7 . The computer-implemented method of claim 1 , further comprising:
modelling, by the server, demand as a negative binomial or Poisson-Gamma distribution.

8 . A system, comprising:
a server comprising a processor and memory and configured to:
receive historical sales data for one or more past time periods and corresponding historical data for one or more causal variables;
deconfound a cause-effect relationship of historical sales data and historical data on the one or more causal variables by conducting one or more randomized controlled A/B group trials;
define one or more sample weights for statistical background subtraction of the historical data;
perform statistical background subtraction on the historical data;
train, by an iterative approach comprising cyclic boosting in additive regression mode, a first machine learning model to predict an absolute individual causal effect on a considered demand quantity; and
predict, with the first machine learning model, an absolute individual causal effect on one or more considered demand quantities during a prediction period by training a second machine learning model.

9 . The system of claim 8 , wherein a casual variable comprises a sending of a personalized coupon.

10 . The system of claim 8 , wherein the server is further configured to:
restrict a learning of a causal factor between a feature describing seasonality over a year and a target to a smooth sinusoidal dependency.

11 . The system of claim 8 , wherein the server is further configured to:
assign one or more positive sample weights and one or more negative sample weights to one or more samples of the one or more randomized controlled A/B group trials.

12 . The system of claim 8 , wherein the server is further configured to:
predict one or more what-if volume predictions from one or more sets of hypothetical causal factors.

13 . The system of claim 8 , wherein the server is further configured to:
predict one or more individual causal effects on gross margin.

14 . The system of claim 8 , wherein the server is further configured to:
model demand as a negative binomial or Poisson-Gamma distribution.

15 . A non-transitory computer-readable storage medium embodied with software, the software when executed configured to:
receive, with a server comprising a processor and memory, historical sales data for one or more past time periods and corresponding historical data for one or more causal variables; deconfound a cause-effect relationship of historical sales data and historical data on the one or more causal variables by conducting one or more randomized controlled A/B group trials; define one or more sample weights for statistical background subtraction of the historical data; perform statistical background subtraction on the historical data; train, by an iterative approach comprising cyclic boosting in additive regression mode, a first machine learning model to predict an absolute individual causal effect on a considered demand quantity; and predict, with the first machine learning model, an absolute individual causal effect on one or more considered demand quantities during a prediction period by training a second machine learning model.

16 . The non-transitory computer-readable storage medium of claim 15 , wherein a casual variable comprises a sending of a personalized coupon.

17 . The non-transitory computer-readable storage medium of claim 15 , wherein the software when executed is further configured to:
restrict a learning of a causal factor between a feature describing seasonality over a year and a target to a smooth sinusoidal dependency.

18 . The non-transitory computer-readable storage medium of claim 15 , wherein the software when executed is further configured to:
assign one or more positive sample weights and one or more negative sample weights to one or more samples of the one or more randomized controlled A/B group trials.

19 . The non-transitory computer-readable storage medium of claim 15 , wherein the software when executed is further configured to:
predict one or more what-if volume predictions from one or more sets of hypothetical causal factors.

20 . The non-transitory computer-readable storage medium of claim 15 , wherein the software when executed is further configured to:
predict one or more individual causal effects on gross margin.

Assignees

• Blue Yonder Group Inc

Inventors

• Wick Felix Christopher
• Feindt Michael

Classifications

• G06N20/00 Primary

  Machine learning · CPC title

• G06Q30/0202 Primary

  Market predictions or forecasting for commercial activities · CPC title

• G06Q30/0207

  Discounts or incentives, e.g. coupons or rebates · CPC title

• G06N5/04

  Inference or reasoning models · CPC title

• G06Q30/0211

  Determining the effectiveness of discounts or incentives · CPC title