Hi everyone, I'm one of the Causal founders — we actually launched a super barebones MVP here on HN in 2019[1], back when we were still tinkering around on nights and weekends, and the reception + signups from that gave us the confidence to quit our jobs and go full-time on this, so thanks for the early support!
The product has come a long way since that first HN launch. The best way to think about it is as a 'multidimensional spreadsheet' — instead of writing formulas that operate on single cells, Causal formulas operate on "variables" that span lots of cells (e.g. multiple 'months', or multiple 'products', or multiple 'countries'), so you can express any kind of model with 100–1000x fewer formulas. Lot of other important functionality like live data integrations, dashboards, etc. but the multi-dimensional modelling system is really the secret sauce :)
Sounds super abstract, but the main use-case today is financial planning/reporting for early-stage companies, although some of our users have actually replaced their BI tools with Causal as well.
Anyway, thanks for the support and keen to hear feedback :)
I am curious about your comment on 100-1000x fewer formulas. Is there a simpler example you can show how your product will do it relative to Excel? When I read your comment, I was thinking, SUM in Excel operates over many cells. You can have Arrays, etc. in Excel. So I am not sure how formulas work in your product that is fundamentally different. Is this like a function that I can define and call that function so it is easier?
Great question! Quite often in financial models, you're dealing with "dimensions" (e.g. your revenue is broken down by product, geography, and time). To model this in Excel you have to write complex SUMIF/INDEX/MATCH formulas. Then you have to drag these formulas over 1000s of cells (if you make a mistake you're screwed, see https://eusprig.org/research-info/horror-stories/).
Causal's building blocks are "variables" and "dimensions" which makes it much more powerful to work with dimensional data.
For your use case, what if you could use Python in Excel with pandas, numpy and the Anaconda ecosystem readily available? How would that change things?
Disclosure: I was a founding member of the Python in Excel team and am looking for new problems that Python in Excel could solve.
IMO this still doesn't change the fact that Excel is a 2D grid. Dealing with multi-dimensional data will always be tricky in that paradigm. Also, you'll still have cell references, no version control, no access control, ...
Excel is an amazing product and I'm sure people will still use it in 10 years. Our thesis is that for financial planning (and various other number-crunching use-cases) our building blocks make more sense.
The UI is definitely similar and no doubt that we're competitors, but under the hood I think we've taken somewhat different approaches to how the product works :)
I've been following your product for some time, and when I visited their website, the app images appeared to be an exact copy of yours. It will be interesting to see how this situation unfolds. Clearly, you seem to have a head start. Good luck.
We've had "fun" solving this for Phosphor which has similar parts to Causal, but is focused more on real assets and emphasizes connection with computable contracts.
For circularity, we found that we could keep the UX dynamic by making a deep copy of the circular part of the DAG behind the scenes, asking the user to determine which variable in that path should be "resolved", hard coding that variable in the copy, then solving that variable to zero through a newton optimization. Once optimized (in parallel to main graph), it feeds back into the main DAG just like any other dependency.
Would be a silly approach in Excel, but not so much here.
Machines like arrays, sequential access, avoiding conditions, and not touching memory unless you need to (compression == performance).
Looks like you've traveled the enlightenment path to array languages! The first time I read Whitney's K language description was a singular mind-expanding moment.
One thing I have to really congratulate you on is your product video.
I honestly think it's one of the best I've seen for any software. It showcases the functionality, displays the actual UI, doesn't rely on narration, is visually interesting, and succinct.
Along with an excellent use of music and pacing. Though it doesn't answer the "is this product for me" question, it does build excitement to make me want to learn more.
Very excited by this and congratulations. I've been an avid causal user on the free plan at the early-stage startup I work for, and used it at my startup before that. It's been so great to see how the product has evolved. I can't overstate what a pleasure it has been to work with even when it was in its earliest stages.
I would absolutely convert to paying if the jump to the next tier weren't so expensive ($250/mo), which has been really tough to digest right now.
Looks great, but I have my reservations about confidentiality of the data.
The financial model, expenses, hiring/ salary plan and basically all thin ledger financials of most SMBs/startups are highly sensitive.
If leaked or sold to third parties, it gives competitors (or maybe your own investors) insights in the technology, development expenses, and direction of the company.
How do you handle data confidentiality? How is the data security?
2. I’ve seen a lot of startups with the business model of serving other startups. These remind me a lot of derivatives in the stock market in terms of the “risk” of their business model, and there have been instances of companies having to pivot when the economy goes down (i.e. Brex)… Do you have a contingency plan for this?
1. It comes from the concept of 'causal inference' in statistics[1] — when we started out, we though the product would go in a much technical/statistical direction, but we were very wrong about that But 'causal' as a word is still relevant to modelling, etc.
2. Fair question haha. While a majority of our customers are startups/tech cos, a bunch of our customers are also non-tech small businesses (e.g. small agencies/consulting firms in various niches). Interestingly, we found that after the economy went down 18 months ago there seemed to be more demand for Causal from smaller companies (startups + regular SMBs) — our guess is that staying on top of finances is important when things are tough, and less important when there's tonnes of cash flying around!
I work in enterprise planning and wonder what is keeping you from targeting larger firms. Is the startup-target temporary or do you expect to let your clients "scale out" of the product?
We do actually have a bunch of bigger companies using Causal (incl. public companies) so it definitely works for bigger companies depending on exactly what they need. If they're looking for a great modelling tool with nice dashboards then I think we're perfect, if they're looking for something with more "workflows" type of stuff with lots of stakeholders who need to contribute to things, then we're less good.
Because of this, we're focusing on smaller companies as a self-serve product for a while, and then gradually plan to build out that kind of enterprise stuff for bigger companies.
There's also a lot more competition in the mid-market/enterprise in this space, and 0 competition for SMBs/self-serve, so we thought it would be good to lean in there :)
Congrats on the 2.0! If I remember right you started as a more general purpose excel competitor, how did you think about doubling down on the financial planning use case?
Thanks! We are still very much a general purpose tool, but until we're a household name like Notion or Airtable, we kind of concluded that we'll need to be much more specific about our messaging/targeting until we reach that critical mass!
Congrats, Causal! I'm a fan and excited to see the 2.0 launch. A big part of our financial planning comes from manual syncing data from the tools we use for billing, expenses, and so on into our excel model — definitely going to try this out.
There seem to be some synergies that you can leverage with spend management and expense management platforms (such as Brex, Ramp, etc.). Is this a fair assumption, and if so, have you considered exploring any opportunities in this area?
Understand this is targetted at financial planning for startups/SMBs, but I'm curious as to if it could be adapted to work for personal financial planning (retirement wealth projections, modelling market downturns)?
Curious if that's a use case you considered or a path the product may go down in the future
Definitely — we have a bunch of users doing that kind of stuff in Causal as well. The product is completely general-purpose (a bit like a spreadsheet), but for business reasons, we're trying to keep our messaging and targeting much more specific to the startups/SMB financial planning use-case :)
First thing I noticed is that the UI looks exactly like Linear. Have seen more companies copy the same UI but not sure if it was actually Linear that started it.
Haha, I think the evolution was something like this:
1. Slack introduces "sidebar" concept
2. Notion takes it a step further
3. Everyone starts doing sidebars, including Linear
We took inspiration from Linear's 'search button in sidebar' and 'profile pic in sidebar', which I think were maybe their unique contributions to the tradition :)
Really, it's just a tab strip, no? (But with fixed heterogeneous tabs, as opposed to a set of homogeneous ones the user adds/removes from as they open/close docs.)
Reminds me of the vertical tab strips in FF w/ TreeStyleTabs.
Yes! Causal natively supports Monte Carlo simulation. E.g. instead of writing "7.5%" in a cell, you can write "5% to 10%" and it will turn that into a distribution, and automatically run a few thousand samples with different values every time it re-calculates.
This alone makes me want to try Causal. I run my business with a gargantuan Google Sheet. It works - we nail our numbers. But scenario testing is very hard.
Nice — definitely try Causal out! We have a 'scenarios' feature that lets you easily spin up "discrete" scenarios, and then you can use the Monte Carlo stuff to see continuous ranges of outcomes.
This reminds me a lot of calculators I use for FIRE, ie the ones shown here [0]. Perhaps I should try Causal for personal use as well as for business use. I've also seen Maybe [1] for personal finances, which looks good too.
It is likely that I won't do that in the case of your program, just letting you know as I just use my own Excel or Google Sheets program. If you would like to include it, please feel free to do so.
Curious to hear what issues you faced (please!). I use FF (on Linux) as my main daily driver and I haven't had any issues with Causal (disclaimer, I work for Causal).
(granted, the experience is optimised for Chromium based browsers, but FF is not forgotten).
The product has come a long way since that first HN launch. The best way to think about it is as a 'multidimensional spreadsheet' — instead of writing formulas that operate on single cells, Causal formulas operate on "variables" that span lots of cells (e.g. multiple 'months', or multiple 'products', or multiple 'countries'), so you can express any kind of model with 100–1000x fewer formulas. Lot of other important functionality like live data integrations, dashboards, etc. but the multi-dimensional modelling system is really the secret sauce :)
Sounds super abstract, but the main use-case today is financial planning/reporting for early-stage companies, although some of our users have actually replaced their BI tools with Causal as well.
Anyway, thanks for the support and keen to hear feedback :)
1: https://news.ycombinator.com/item?id=19704418