Paper: Monte Carlo Experiments of Network Effects in Randomized Controlled Trials

Marton Trencseni - Tue 09 April 2024 • Tagged with ab-testing

I run Monte Carlo simulations of content production over random Watts-Strogatz graphs to show various effects relevant to modeling and understanding Randomized Controlled Trials on social networks: the network effect, spillover effect, experiment dampening effect, intrinsic dampening effect, clustering effect, degree distribution effect and the experiment size effect. I will also define some simple metrics to measure their strength. When running experiments these potentially unexpected effects must be understood and controlled for in some manner, such as modeling the underlying graph structure to establish a baseline.

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Real-world experiments: 5 Lessons from Google, Bing, Netflix and Alibaba

Marton Trencseni - Sun 18 June 2023 • Tagged with ab-testing

I discuss five lessons from large-scale experiments conducted by Google, Bing, Netflix and Alibaba: Kohavi's 1 out of 3 rule, Google's 41 shades of blue, Bing's unexpected big win, Alibaba's personalization experiment and Netflix' movie image personalization.


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Five ways to reduce variance in A/B testing

Marton Trencseni - Sun 19 September 2021 • Tagged with ab-testing, variance, stratification, cuped

I use toy Monte Carlo simulations to demonstrate 5 ways to reduce variance in A/B testing: increase sample size, move towards a more even split, reduce variance in the metric definition, stratification and CUPED.

Historic lift

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Correlations, seasonality, lift and CUPED

Marton Trencseni - Sun 05 September 2021 • Tagged with ab-testing, cuped

In this final blog post about CUPED, I will address some questions about CUPED, such as, is correlation between "before" and "after" the same as seasonality?


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A/A testing and false positives with CUPED

Marton Trencseni - Sun 15 August 2021 • Tagged with ab-testing, cuped

I use Monte Carlo simulations of A/A tests to demonstrate how Data Scientists can incorrectly skew lift and p-values if they pick-and-choose between reporting traditional and CUPED results after the experiment has concluded.

Historic lift

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Reducing variance in conversion A/B testing with CUPED

Marton Trencseni - Sat 07 August 2021 • Tagged with ab-testing, cuped

I use Monte Carlo simulations of conversion A/B tests to demonstrate how CUPED reduces measurement variance in conversion experiments.

Historic lift

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Reducing variance in A/B testing with CUPED

Marton Trencseni - Sat 31 July 2021 • Tagged with ab-testing, cuped

I use Monte Carlo simulations of A/B tests to demonstrate CUPED, a method to use historic "before" data to reduce the variance in the measurement of the treatment lift.

Historic lift

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Building intuition for p-values and statistical significance

Marton Trencseni - Sun 25 April 2021 • Tagged with ab-testing

This is the transcript of a talk I did on experimentation and A/B testing to give the audience an intuitive understanding of p-values and statistical significance.

Coin flip

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Making statistics lie for the 2020 Presidential election

Marton Trencseni - Thu 17 December 2020 • Tagged with ab-testing, trump, politics

After the 2020 US presidential election, the Trump campaign filed over 50 lawsuits and attacked the integrity of the elections by claiming there was voter fraud. One of the last lawsuits was filed in the Supreme Court of the United States by the state of Texas. Here I look at the statistical claims made in this lawsuit that were supposed to show irregularities in the Georgia vote.

Trump vs Biden

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Comparing conversion at control and treatment sites

Marton Trencseni - Thu 03 December 2020 • Tagged with ab-testing

In real-life, non-digital situations, it's often not feasible to run true A/B tests. In such cases, we can compare before and after rollout conversions at a treatment site, while using a similar control site to measure and correct for seasonality. The post discusses how to compute increasingly correct p-values and bayesian probabilities in such scenarios.

Monte Carlo simulated control lifts

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Multi-armed bandits and false positives

Marton Trencseni - Fri 21 August 2020 • Tagged with ab-testing

I use Monte Carlo simulations to explore the false positive rate of Multi-armed bandits.


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A/B testing and Multi-armed bandits

Marton Trencseni - Fri 07 August 2020 • Tagged with ab-testing

Multi-armed bandits minimize regret when performing A/B tests, trading off between exploration and exploitation. Monte Carlo simulations shows that less exploration yields less statistical significance.


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Understanding Facebook’s Planout A/B testing framework

Marton Trencseni - Fri 22 May 2020 • Tagged with ab-testing

PlanOut is a framework for online field experiments. It was created by Facebook in 2014 to make it easy to run and iterate on sophisticated experiments in a statistically sound manner.


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Validation checks for A/B tests

Marton Trencseni - Thu 16 April 2020 • Tagged with ab-testing

A/B tests go wrong all the time, even in sophisticated product teams. As this article shows, for a range of problems we can run automated validation checks to catch problems early, before they have too bad of an effect on customers or the business. These validation checks compare various statistical properties of the funnels A and B to catch likely problems. Large technology companies are running such validation checks automatically and continuously for their online experiments.

Kolmogorov-Smirnov test

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Running multiple A/B tests in parallel

Marton Trencseni - Mon 06 April 2020 • Tagged with ab-testing

I show using Monte Carlo simulations that randomizing user assignments into A/B test experiments makes it possible to run multiple A/B tests at once and measure accurate lifts on the same metric, assuming the experiments are independent.


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Bayesian A/B conversion tests

Marton Trencseni - Tue 31 March 2020 • Tagged with bayesian, ab-testing

I compare probabilities from Bayesian A/B testing with Beta distributions to frequentist A/B tests using Monte Carlo simulations. Under a lot of circumstances, the bayesian probability of the action hypothesis being true and the frequentist p value are complementary.

Bayes vs z-test

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A/B testing and the G-test

Marton Trencseni - Mon 23 March 2020 • Tagged with ab-testing

The G-test for conversion A/B tests is similar to the Chi-squared test. Monte-Carlo simulations show that the two are indistinguishable in practice.

G-test vs Chi-squared p differences

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A/B testing and networks effects

Marton Trencseni - Sat 21 March 2020 • Tagged with ab-testing

I use Monte Carlo simulations to explore how A/B testing on Watts–Strogatz random graphs depends on the degree distribution of the social network.

Watts-Strogatz degree distribution

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A/B testing on social networks

Marton Trencseni - Mon 09 March 2020 • Tagged with ab-testing

I use Monte Carlo simulations to show that experimentation on social networks is a beatiful statistical problem with unexpected nuances due to network effects.


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Early stopping in A/B testing

Marton Trencseni - Thu 05 March 2020 • Tagged with ab-testing

Increased false positive rate due to early stopping is beautiful nuance of statistical testing. It is equivalent to running at an overall higher alpha. Data scientists need to be aware of this phenomenon so they can control it and keep their organizations honest about their experimental results.

Early stopping

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