Computerized system for increasing computational efficiency in evaluation and selecting propensity score models

1 . A system comprising:

a memory for storing:

first data representing a plurality of characteristics of each of a plurality of test subjects, and

second data representing a plurality of candidate propensity score models for estimating an effectiveness of a treatment performed on one or more of the test subjects, with the second data specifying an ordering of the plurality of candidate propensity score models; and

a processor communicatively coupled to the at least one memory, wherein the processor is configured to:

access, from the memory, the first data and the second data;

select a set of propensity score models from among the plurality of candidate propensity score models, wherein selecting the set of propensity score models comprises:

automatically sequentially evaluating at least some of the candidate propensity score models until one or more stop criteria are satisfied, wherein the one or more stop criteria comprises a determination that a difference of each of one or more characteristics of the candidate propensity score model satisfies a threshold value, wherein evaluating each of the candidate propensity score models comprises:

in accordance with the second data, accessing, by the system, computer code to generate a candidate propensity score model that is ordered next in the ordering;

executing, by the system, the computer code to obtain an output of the candidate propensity score model based on at least a portion of the first data as input, and

determining whether the output of the candidate propensity score model satisfies the one or more stop criteria; and

selecting the set of propensity score models based on the sequential evaluation of at least some of the candidate propensity score models;

store, using the memory, a data structure representing the set of propensity score models; and

output the data structure, wherein outputting the data structure comprises causing a user interface to be presented to a user, wherein the user interface comprises an indication of the set of propensity score models.

2 . The system of claim 1 , wherein the system is further configured to:

estimate the effectiveness of the treatment based on the set of propensity score models.

3 . A method comprising:

accessing, by a computer system from one or more hardware storage devices:

first data representing a plurality of characteristics of each of a plurality of test subjects, and

second data representing a plurality of candidate propensity score models for estimating an effectiveness of a treatment of one or more of the plurality of test subjects, with the second data specifying an ordering of the plurality of candidate propensity score models;

selecting, by the computer system, a set of propensity score models from among the plurality of candidate propensity score models, wherein selecting the set of propensity score models comprises:

automatically sequentially evaluating by the computer system at least some of the candidate propensity score models until one or more stop criteria are satisfied, wherein the one or more stop criteria comprises a determination that a difference of each of one or more characteristics of the candidate propensity score model satisfies a threshold value, wherein evaluating each of the candidate propensity score models comprises:

in accordance with the second data, accessing, by the computer system, computer code to generate a candidate propensity score model that is ordered next in the ordering;

executing, by the computer system, the computer code to obtain an output of the candidate propensity score model based on at least a portion of the first data as input, and

determining whether the output of the candidate propensity score model satisfies the one or more stop criteria; and

selecting the set of propensity score models based on the sequential evaluation of at least some of the candidate propensity score models;

storing, by the computer system, a data structure representing the set of propensity score models using the one or more hardware storage devices; and

outputting, by the computer system, the data structure, wherein outputting the data structure comprises causing, by the computer system, a user interface to be presented to a user, wherein the user interface comprises an indication of the set of propensity score models.

4 . The method of claim 3 , further comprising estimating the effectiveness of the treatment based on the set of propensity score models.

5 . The method of claim 4 , wherein estimating the effectiveness of the treatment of the clinical study comprises:

minimizing, based on the at least one propensity score model of the set of propensity score models, a selection bias associated with an estimation of an effect of the treatment.

6 . The method of claim 3 , further comprising:

conducting a clinical study based on the data structure.

7 . The method of claim 3 , further comprising:

modifying the treatment of one or more additional subjects based on the data structure.

8 . The method of claim 3 , wherein the set of propensity score models consists of one propensity score model.

9 . The method of claim 3 , wherein the set of propensity score models comprises a plurality of propensity score models.

10 . The method of claim 3 , wherein sequentially evaluating at least some of the candidate propensity score models comprises:

determining a sequential order for the candidate propensity score models, and

evaluating at least some of the candidate propensity score models based on the sequential order.

11 . The method of claim 9 , wherein the sequential order is determined based on a complexity of each of the candidate propensity score models.

12 . The method of claim 9 , wherein the sequential order is determined based on a statistical complexity of each of the candidate propensity score models.

13 . The method of claim 2 , wherein the output of the candidate propensity score model represents a standardized difference of each of one or more covariates of the candidate propensity score model.

14 . The method of claim 12 , wherein the one or more stop criteria comprises a determination that the standardized difference of each of the one or more covariates of the candidate propensity score model is less than a threshold value.

15 . The method of claim 12 , wherein the one or more stop criteria comprises a determination that a number of evaluated candidate propensity score models is greater than or equal to a threshold value.

16 . The method of claim 14 , wherein the data structure comprises a tabular structured data file including a grid of data cells,

wherein the data cells are arranged according to a plurality of rows and a plurality of columns, and

wherein the data cells represent:

at least one of the candidate propensity score models, and

at least one of the standardized differences corresponding to at least one of the candidate propensity score models.

17 . The method of claim 3 , further comprising:

receiving user input representing one or more selection criteria, and

selecting at least some of the candidate propensity score models based on the one or more selection criteria.

18 . The method of claim 3 , wherein each of the candidate propensity score models is configured to generate a respective propensity score based on at least a portion of the first data, and wherein the propensity score represents a probability that a particular test subject has been administered the treatment based on the characteristics of that test subject.

19 . One or more non-transitory computer-readable media storing instructions which, when executed by at least one processor, cause the at least one processor to perform operations comprising:

accessing, from one or more hardware storage devices:

first data representing a plurality of characteristics of each of a plurality of test subjects, and

second data representing a plurality of candidate propensity score models for estimating an effectiveness of a treatment of one or more of the plurality of test subjects, with the second data specifying an ordering of the plurality of candidate propensity score models;

selecting a set of propensity score models from among the plurality of candidate propensity score models, wherein selecting the set of propensity score models comprises:

automatically sequentially evaluating at least some of the candidate propensity score models until one or more stop criteria are satisfied, wherein the one or more stop criteria comprises a determination that a difference of each of one or more characteristics of the candidate propensity score model satisfies a threshold value, wherein evaluating each of the candidate propensity score models comprises:

in accordance with the second data, accessing computer code to generate a candidate propensity score model that is ordered next in the ordering;

executing the computer code to obtain an output of the candidate propensity score model based on at least a portion of the first data as input, and

determining whether the output of the candidate propensity score model satisfies the one or more stop criteria; and

selecting the set of propensity score models based on the sequential evaluation of at least some of the candidate propensity score models;

storing a data structure representing the set of propensity score models using the one or more hardware storage devices; and

outputting the data structure, wherein outputting the data structure comprises causing a user interface to be presented to a user, wherein the user interface comprises an indication of the set of propensity score models.

20 . The one or more non-transitory computer-readable media of claim 19 , wherein the operations further comprise:

estimating the effectiveness of the treatment based on the set of propensity score models.