Readme

The codebase contains techniques for identifying positivity violation. It also contains code for estimating the effect of injectables on discontinuation and characterizing overlap regions.

data contains the datasets

• <ctry_code>_Preprocessed.csv - preprocessed file - including censored records.
• <ctry_code>_transcript.txt - support overlap transcript
• encoding.csv - selected features and their encoding
• fp_select<timestamp>.csv - encoded and feature selected subset of the data for each country in the following order (et, ng, sl, br, zm, lb, ug)
• varencoding-<ctry_code> - DHS encoding of the features

figures contains the plots for all the experiments

• causaleval<timestamp>.pdf - evaluation plot ordered by ipw; (et, ng, sl, br, zm, lb, ug), ipw overlap (et, ng, sl, br, zm, lb, ug), ow;(et, ng, sl, br, zm, lb, ug), ow overlap (et, ng, sl, br, zm, lb, ug)
• effects<timestamp>.pdf - distribution of ATE in the same order as above
• outcomes<timestamp>.pdf - distribution of marginal effect in the same order as above
• placeboeffects<timestamp>.pdf - distribution of placebo effect in the same order as above
• supportaccuracy<timestamp>.pdf - plot of hyperparameter search in the same country order
• supportclause<>timestamp.pdf - plot of number of literals vs number of clauses
• rulesets.pptx rulesets figures in editable form

mdscan - Multidimensional Subset Scanning technique (link)

notebooks - some notebooks for positivity violation experimentation - e.g wanted to see if we could apply subset scanning (1d) to the bottleneck layer of an auto-encoder to identify deviations between treated and control groups. The embedding was also useful for applying IRM.

overrule - OverRule: Overlap Estimation using Rule Sets (link)

positivitree - Positivitree: Finding and characterizing positivity violations using decision trees (link)

OW.py - implementation of overlap weighting

comp_causalmodel.py - causal modelling methods - effect estimation and evaluation

comp_overrule.py - overrule methods for support ruleset estimation, getting the optimal parsimonious hyper parameter, and helper for getting index of overlap violations

comp_overrule_clr.py - overrule methods for propensity overlap ruleset estimation - with calibrated logistic regression

comp_overrule_knn.py - overrule methods for propensity overlap ruleset estimation - with knn

comp_postivitree.py - positivitree methods for learning rulesets

comp_preprocessing.py - methods for encoding the covariates and and obtaining the DHS encoding

hypsearch.npy - full results from Overrule hyper-parameter search

overrule_exps.ipynb overrule experiments

ptree_exps.ipynb causal modelling and positivitree experiments

riskratios.txt ATEs in ratio form - for all the experiments

utils.py a number of helper functions to transcribe rulesets, to read or write models or files