Comments (5)
There are two formulations. One that averages households vs one that averages individuals. Perez-Heydrich et al is the household, and Liu et al is the individual. Differences are described in Basse and Feller 2018 (JASA). Both option should be implemented. During the class initialization, should have some option like inference_level='household'
and inference_level='individual'
with an explanation in the documentation that this is only important if the group sizes are different.
Ultimately, the choice should be guided by what you want to infer from the data
from zepid.
Household version:
https://github.com/bsaul/inferference/blob/master/R/integrands.R
Individual version:
https://github.com/BarkleyBG/stabilizedinterference
from zepid.
Also will add Network-TMLE to this branch. Need to decide on a name. Should have influence curve and parametric bootstrap options for CI (parametric CI seems to perform a little better in this paper).
Ideally, this class would be fed a network and extract the relevant information from the network by itself. I have some other functions in some simulations that can already do this. Will have to name the options (probably a list of options). Also need to allow custom specifications (like custom measures of centrality or the like).
https://arxiv.org/abs/1705.08527
Now part of Issue #29
from zepid.
For integration, will be zepid.causal.interference
. Within branch, will have structured
and general
for the two distinct formulations of interference.
from zepid.
Due to various other requirements (statsmodels 0.10.0) and the less broad use-case for interference methods, this will become its own library. This applies to all interference causal inference methods
from zepid.
Related Issues (20)
- IPTW handle PerfectSeparationErrors in the marginal structural model better
- AIPW for survival analysis ? HOT 1
- Dual treatments
- ValueError better pytest strategy
- Package compatibility? HOT 2
- Update documentation (and possibly re-organize) HOT 2
- MonteCarloGFormula
- Add Odds Ratio and other estimands for AIPTW and TMLE
- Addition of meta-analysis tools
- add p-value column in a forrest plot/ effectmeasureplot HOT 2
- Enhancement in graphics.py to change odds text size HOT 1
- Saving DAGs programatically HOT 11
- sklearn dependancy in setup.py should be scikit-learn HOT 1
- AIPW formula equivalent to what's in the literature? HOT 2
- Perfect separation error for using `SingleCrossfitTMLE` HOT 2
- Superlearn check weights HOT 2
- SingleCrossFit `invalid value encountered in log` HOT 8
- Unable to install latest 0.9.0 version through pip HOT 7
- Risk Ratio Summary HOT 1
- The default regression argument of zepid.base.interaction_contrast_ratio differs from the description in the documentation. HOT 4
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 zepid.