Re: [Scikit-learn-general] Scikit Learn, Tree, new criterion

[Gilles Louppe]

Hi Eskil,

(CC: the scikit-learn mailing list)

Unfortunately, I would not have time myself to implement this new
criterion. In any case, given the recent publication of this paper, I
dont think we would add it to the scikit-learn codebase. Our policy is
to only include time-tested algorithms.  That being said, maybe
someone from the mailing list would be interested in helping you
implementing this criterion in a separate fork.

Best,
Gilles

On 14 March 2016 at 22:48, Eskil Forsell
 wrote:
> Dear Gilles,
> I'm writing to you as the first author of the tree module in scikit-learn to
> gauge your interest in implementing a novel and really useful (at least for
> policy oriented economists like me) splitting criterion.
>
> I'm a PhD student in economics at Stockholm School of Economics and my
> research and work focuses largely on evaluating policy by using randomised
> controlled trials. There has recently been a lot of buzz in the field of
> economics of the potential intersection of machine learning and causal
> inference. Much of this buzz has been inspired by a paper outlining how to
> use a splitting criteria tailored to the idea that the splits will later be
> used as subpopulation for estimating treatment effects on a hold-out sample,
> thus yielding correct standard errors. (I'm attaching the paper.)
>
> The authors are working on implementing the criterion in R but haven't yet
> released anything publicly. I really believe that this method of estimating
> heterogenous causal effects will be extremely popular among empirical
> economists and potentially be of great use to policy-makers who want to
> figure out how interventions work differently depending on personal
> characteristics.
>
> I had a look at the criterion file but quickly realized that this wouldn't
> be something I could implement myself. If you're interested I'd love to talk
> more about it. If you're not interested, perhaps you could point me in the
> direction of someone who might be and who'd have no problem of implementing
> the criterion? Based on my understanding of the paper it's actually a quite
> simple extension of the MSE criterion slightly complicated by the fact that
> instead of raw means, we're using treatment effects (which crucially depend
> on a treatment indicator variable).
>
> All the best and hope to hear from you soon.
>
> Regards,
> Eskil

--
Transform Data into Opportunity.
Accelerate data analysis in your applications with
Intel Data Analytics Acceleration Library.
Click to learn more.
http://pubads.g.doubleclick.net/gampad/clk?id=278785231=/4140
___
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general

[Scikit-learn-general] Google Summer of Code

[Manoj Kumar]

Hi,

We have decided to take part in Google Summer of Code yet again, under the
PSF. This time we have just one idea (
https://github.com/scikit-learn/scikit-learn/wiki/Google-summer-of-code-%28GSOC%29-2016)
because almost all the core developers are busy this summer.

Students who are willing to apply can either directly to
http://summerofcode.withgoogle.com/ or link their applications in the wiki
https://github.com/scikit-learn/scikit-learn/wiki . The deadline is on
March 25th. Remember that the PSF has a patch requirement but it is highly
likely that the selected student has already made high quality
contributions.

-- 
Manoj,
http://github.com/MechCoder
--
Transform Data into Opportunity.
Accelerate data analysis in your applications with
Intel Data Analytics Acceleration Library.
Click to learn more.
http://pubads.g.doubleclick.net/gampad/clk?id=278785231=/4140___
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general