Bidding Systems and Methods For Minimizing The Cost Of Field Experiments Using Advertisement Exchanges

1 . A bidding system comprising:

a processor and a non-transitory storage medium accessible to the processor;

a memory storing a database comprising bidding parameters;

a computer server in communication with the memory and the database, the computer server comprising:

circuitry configured to obtain a bidding opportunity to deliver a message from an exchange system, wherein the bidding opportunity comprises an impression candidate and user information associated with the impression candidate;

circuitry configured to obtain at least one bidding parameters from the database, wherein the at least one bidding parameters indicates a target audience of the message; and

circuitry configured to assign a random bid amount to the bidding opportunity based on the at least one bidding parameters, wherein the random bid amount at least partially indicates a treatment intensity of the message.

2 . The bidding system of claim 1 , further comprising:

circuitry configured to use the random bid amount as an instrumental variable to estimate a treatment effect of the message.

3 . The bidding system of claim 1 , further comprising:

circuitry configured to assess each bidding opportunity by comparing similarities between the user information and the at least one bidding parameters, wherein a decision to bid on the bidding opportunity indicates an intent to treat.

4 . The bidding system of claim 1 , wherein the message comprises an advertisement; and wherein the computer server further comprises circuitry configured to assign the random bid amount in a manner such that the random bid amount is correlated with treatment intensity of the advertisement and the random bid amount is not correlated with unobserved user features correlated with the treatment intensity.

5 . The bidding system of claim 2 , further comprising:

circuitry configured to estimate the treatment effect using a regression model.

6 . The bidding system of claim 5 , wherein the computer further comprises circuitry configured to estimate the regression model using two-stage least-squares regression analysis.

7 . The bidding system of claim 1 , wherein the computer server further comprises circuitry configured to record a plurality of random bid amounts and a plurality of responses respectively corresponding to the plurality of random bid amounts.

8 . The bidding system of claim 7 , further comprising:

circuitry configured to regress treatment on the plurality of random bid amounts and obtain a first regression model configured to determine a plurality of treatment estimates; and

circuitry configured to obtain a second regression model by regressing the plurality of responses on the plurality of treatment estimates using the first regression model,

wherein the first and second regression models are characterized by unobserved scalar parameters configured to maximize likelihood functions according to the first and second regression models.

9 . A method, comprising:

obtaining, by one or more devices having a processor, a bidding opportunity to deliver a message from an exchange system, wherein the bidding opportunity comprises an impression candidate and user information associated with the impression candidate;

obtaining, by the one or more devices, at least one bidding parameters from a database, wherein the at least one bidding parameters indicates a target audience of the message; and

assigning, by the one or more devices, a random bid amount to the bidding opportunity based on the at least one bidding parameters, wherein the random bid amount at least partially indicates a treatment intensity of the message.

10 . The method of claim 9 , further comprising:

using, by the one or more devices, the random bid amount as an instrumental variable to estimate a treatment effect of the message.

11 . The method of claim 9 , further comprising:

assessing each bidding opportunity by comparing similarities between the user information and the at least one bidding parameters, wherein a decision to bid on the bidding opportunity indicates an intent to treat.

12 . The method of claim 9 , further comprising:

assigning the random bid amount in a manner such that the random bid amount is correlated with treatment intensity of an advertisement in the message and the random bid amount is not correlated with unobserved user features correlated with the treatment intensity.

13 . The method of claim 10 , further comprising:

estimating the treatment effect using a regression model.

14 . The method of claim 13 , further comprising:

estimating the regression model using two-stage least-squares regression analysis.

15 . The method of claim 9 , further comprising:

recording a plurality of random bid amounts and a plurality of responses respectively corresponding to the plurality of random bid amounts.

16 . The method of claim 15 , further comprising:

regressing treatment on the plurality of random bid amounts and obtain a first regression model configured to determine a plurality of treatment estimates; and

obtaining a second regression model by regressing the plurality of responses on the plurality of treatment estimates using the first regression model,

wherein the first and second regression models are characterized by unobserved scalar parameters configured to maximize likelihood functions according to the first and second regression models.

17 . A non-transitory storage medium, comprising:

instructions executable to obtain a bidding opportunity to deliver a message from an exchange system, wherein the bidding opportunity comprises an impression candidate and user information associated with the impression candidate;

instructions executable to obtain at least one bidding parameters for an advertiser from a database, wherein the at least one bidding parameters indicates a target audience of the message;

instructions executable to assign a random bid amount to the bidding opportunity based on the at least one bidding parameters in a manner such that the random bid amount is correlated with treatment intensity of an advertisement in the message and the random bid amount is not correlated with unobserved user features correlated with the treatment intensity; and

instructions executable to use the random bid amount to estimate advertisement treatment effect within a regression model.

18 . The non-transitory storage medium of claim 17 , further comprising:

instructions executable to use the random bid amount as an instrumental variable to estimate the advertisement treatment effect within the regression model; and

instructions executable to estimate the advertisement treatment effect using two-stage least-squares regression analysis.

19 . The non-transitory storage medium of claim 17 , further comprising:

instructions executable to record a plurality of random bid amounts and a plurality of responses respectively corresponding to the plurality of random bid amounts.

20 . The non-transitory storage medium of claim 19 , further comprising:

instructions executable to regress treatment on the plurality of random bid amounts and obtain a first regression model configured to determine a plurality of treatment estimates; and

instructions executable to obtain a second regression model by regressing the plurality of responses on the plurality of treatment estimates using the first regression model;

wherein the first and second regression models are characterized by unobserved scalar parameters configured to maximize likelihood functions according to the first and second regression models.