Comments (2)
Hi Winston,
Thanks for pointing out that we were missing a tag for our 0.15.0 release - I've added it. We added a number of new features to this release, but I don't see any obvious way that they would affect your code (and our example notebook generates virtually identical results for CausalForestDML before and after the change from 0.14.1 to 0.15.0). In the meantime I'll try to see if I can reproduce the issue, but if you can produce a synthetic dataset that demonstrates the issue that would save a lot of time.
from econml.
@kbattocchi I just found out that there is a small discrepancy in my sample weight inputs in my experiments using the 0.15.0 version. Fixing that, bothin produces identical results now. Thanks for adding the release notes.
from econml.
Related Issues (20)
- Is a feature engineered from treatment T another treatment to consider for CATE?
- Will DRIV be able to support multiple treatments via multiple instruments?
- DynamicDML() issue: AttributeError: Provided crossfit folds contain training splits that don't contain all treatments DynamicDML HOT 5
- Inconsistent ATE estimation HOT 3
- Confidence Interval for categorical outcome HOT 3
- [Bug] fit_cate_incercept argument in econml.dml.DML does not add intercept correctly HOT 5
- `shap_values` for tree-based models doesn't set `check_additivity=False` as expected HOT 3
- A column-vector y was passed when a 1d array was expected (however, y is already a 1d array) HOT 1
- Individual Treatment Effects HOT 1
- How to get the Confidence Interval for ATE instead of CATE HOT 1
- Converting to Python object not allowed without gil HOT 1
- Reproducible error: SHAP ExplainerError: Additivity check failed in TreeExplainer HOT 4
- Questions regarding DRPolicyForest results HOT 2
- DRtester does not work for binary treatment AND binary outcome HOT 4
- Confounder adjusting before applying the ITE model to observational data
- Calculation of confidence intervals in NormalInferenceResults becomes very slow when passing big dataframes HOT 2
- DML discrete outcome HOT 1
- High memory footprint for big dataframes in CausalForest model HOT 3
- Questions about econml and CausalForestDML
- Reduce residual confounding in time series
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from econml.