Advanced Matching Algorithm for Precise A/B Testing
klastroTest is Available on Many Areas!
“Unlock Your Design’s Potential: Increase sales with klastroTest.
klastroTest uses our unique matching algorithm that considers all features of user data.”
“No more propensity distance matching. klastroTest uses a better solution to define your control and treatment groups.
Also, klastroTest offers automatic calculations of ATE.”
klastroTest
Find your winning design with precise A/B testing for advertising and design
Why klastroTest?
While Propensity Score Matching (PSM) is a popular technique for reducing bias in observational studies, it has its own limitations. We do not use Propensity Score Matching. klastroTest observes all features of the input data and matches the control with treatment groups.
A/B Test Results in 3 Seconds? The Wait is Over!
In the fast-paced world of retail, timing is everything. Our solution, over 1000x faster than the competition, delivers A/B test results in just 3 seconds, empowering you to make real-time, data-driven decisions. Optimize campaigns daily and stay ahead of trends—no more waiting.
GPU Accelerated
Our benchmarks show our advanced solution processing matching tasks with thousands of data points in milliseconds—something that takes traditional methods over an hour. Experience 1300x faster speeds with 2,500 x 8 of experiment data and 2200x faster speeds with 50,000 x 8 data!*
The more data, the greater the speed advantage.
*While our containerized environment demonstrates speeds under 3 seconds, real-world performance may vary due to network conditions and other factors that influence communication times.
(Experiment group: 2,517 x 8, Control group: 8,461 x 8 – Traditional solution: 58 min 28 sec vs. klastroTest: 2.57 sec)
(Experiment group: 49,825 x 8, Control group: 56,153 x 8 – Traditional solution: 6 days 12 hrs 49 min 17 sec vs. klastroTest: 4 min 7 sec)
Missing Confounders? Not a Problem
PSM relies on observed variables to estimate the propensity score, which represents the probability of an individual receiving a treatment. If crucial confounding variables are missing from the dataset, the propensity score will be inaccurate, leading to imperfect matching and biased treatment effect estimates.
In contrast, klastroTest ensures comprehensive matching without missing confounders, delivering more reliable treatment effect estimates.
Optimized for High-Dimensional Data Matching
As the number of variables increases, accurately estimating the propensity score becomes more complex. In high-dimensional data, the “curse of dimensionality” can hinder PSM’s ability to effectively match individuals, potentially increasing bias and reducing the efficiency of the analysis.
Howerver, klastroTest handles high-dimensional data with ease, avoiding the pitfalls of traditional methods.
Adaptable Matching Without Linear Assumptions
Many PSM implementations utilize logistic regression to predict propensity scores. Logistic regression assumes a linear relationship between the variables and the outcome. However, this assumption might not hold true in all cases, leading to inaccurate propensity score estimation and subsequent matching errors.
Unlike traditional methods, klastroTest requires no linear assumptions, ensuring flexibility and accuracy across diverse datasets.
klastroTest is Simpler
klastroTest is conceptually simpler and computationally less demanding than PSM. It doesn’t require complex modeling or estimation procedures, making it easier for researchers to implement. This can reduce the potential for errors and biases that can arise during the propensity score estimation process in PSM.
klastroTest
Get Your Perfect Control and Treatment Groups
Effectively Handles Multi-Dimensional Data
PSM primarily relies on a single propensity score, which may not fully reflect the nuances of multi-dimensional medical data. klastroTest accounts for correlations between variables when calculating distances. This allows for more accurate matching in multi-dimensional data by capturing the complex relationships between variables, such as patient demographics, comorbidities, and lab results.
Superior Covariate Balance
klastroTest effectively balances multiple covariates simultaneously. In contrast, PSM relies on a single propensity score, which may not guarantee balance for individual covariates. By achieving better covariate balance between treatment and control groups, klastroTest can further reduce selection bias and improve the accuracy of treatment effect estimation.
Facilitates Subgroup Analysis
klastroTest is particularly useful for analyzing specific patient subgroups. For instance, when investigating treatment effects in patients with severe disease, researchers can utilize klastroTest to identify and compare similar patients based on relevant characteristics. PSM, which focuses on the overall propensity score distribution, may not be ideal for such targeted analyses.
Automated ATE Calculation for Fast and Accurate Clinical Data Analysis
klastroTest automatically calculates the Average Treatment Effect (ATE), a key metric in clinical research, simplifying complex data analysis and providing rapid results. Traditional ATE calculation requires extensive manual work, including variable setup, complex algorithm application, and error checking. klastroTest automates this entire process, enabling researchers to perform faster and more accurate analyses.
As soon as matching is complete and treatment information is available, klastroTest calculates the ATE instantly, delivering results on the spot.
This feature saves researchers time and effort, providing reliable insights with remarkable speed.
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