Show HN: ClearBrain (YC W18) – Automated Causal Analytics

Our founding team worked on this problem for quite a few years while at Google and Optimizely. We contributed to Google Analytics to analyze historical behaviors in seconds, but observing historical trends merely produced noisy correlations. We built Optimizely to measure true cause and effect through A/B testing, but tests took 4-6 weeks on avg to reach significance, and so it would take years to measure the impact of every single page or feature in an app.

So we asked ourselves, could we estimate which in-app behaviors cause conversion, to complement (not replace) a traditional A/B test? We spent a year in R&D, and built ClearBrain as a self-serve “causal analytics” platform. All you have to do is specify a goal - signup, engagement, purchase - and ClearBrain ranks which behaviors are most likely to cause conversion.

Building this required a mix of real-time processing + auto ML + algorithm work. We connect to a company’s app data via Segment, and ingest their app events in real-time via Cloud Dataflow into a BigQuery backend. When a customer uses the ClearBrain UI to select a specific app event as their conversion goal, our backend will automatically run multiple observational studies to analyze how every other app event may cause that goal. This is done in parallel using SparkML, to analyze thousands of different events in minutes. (more on our algorithm here: https://blog.clearbrain.com/posts/introducing-causal-analyti...)

We’ve had beta customers like Chime Bank, InVision, and TravelBank use ClearBrain to estimate which behaviors and landing pages cause their users to convert, and in turn prioritize their actual growth and A/B testing efforts there.

We’re now releasing the product into general availability in partnership with Segment - available on a free self-serve basis today! We look forward to feedback from the HN community. :)

I'm asking not as knock against your service, but genuine curiosity about how you manage to solve this incredibly hard problem.

EDIT: From your white paper, it looks like you're running a regression that controls for a bunch of confounders. You also interact the treatment variable with those confounders to get the heterogeneous treatment effect.

My concern with that is that we're not controlling for unobservable confounders, which make causal inference so difficult. If we assume that controlling for observable confounders is enough (we shouldn't!), then correlation and causation are the same.

White paper: https://blog.clearbrain.com/posts/introducing-causal-analyti...

Extraordinary claims require extraordinary evidence. How many of your estimated treatment effects have been supported by experiments? Do you have experiments demonstrating that your model generalizes? How accurate are your estimates compared to experimental results?

It's ironic that you're marketing a causal + analytics product without any data. Generating a narrative and basing it off of observational data is the typical trap that many causal claims fall into. Portraying yourselves as statistical experts and pushing unsubstantiated claims is misleading bordering on unethical.

I think I reached out to you in early 2018. Any news about the integration without segment (e.g. Amplitude in our case)? And what’s the pricing model? Couldn’t find much on the site (maybe it’s more limited on mobile?)