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README.md

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-# confounding-robust-policy-improvement
+# confounding-robust-policy-improvement
+
+
+
+```ConfoundingRobustPolicy``` is a wrapper object. The main choices are: 
+- Optimizer
+  - Gradient-based approaches
+  - Global optimization
+- Uncertainty set and weight subproblem solution
+  - This is passed to the gradient-based approach as a function callback for computing the optimal weights. 
+- Performance Evaluation
+  - simulation (known dgp) 
+  - observational data (pass in propensity scores, evaluate by IPW) 
+- Type of treatment
+  - binary treatment (we reparametrize with respect to the probability of assigning ```T=1```)
+  - multiple treatments
+
+
+The ```.fit()``` method is specialized to handle various combinations of the above configurations. 
+
+```ConfoundingRobustPolicy``` takes as input: 
+- ```baseline_pol``` (function returning baseline policy) 
+
+
+```ConfoundingRobustPolicy.fit()``` takes as input: 
+- ```X``` data
+- ```T``` data (integer-coded) 
+- ```Y```
+- ```q0``` nominal propensities
+- ```log gamma``` series of sensitivity parameters to optimize over (some approaches leverage the nested structure of uncertainty sets
+- ```optimization params``` a dictionary of optimization parameters
+- ```eval_conf``` a dictionary of evaluation parameters. 
+
+ ```optimization params``` is a dictionary with the following configuration parameters: 
+-```optimizer```: function callback, e.g. ```get_opt_tree_policy```
+-```pol_opt```: name of policy class 
+- ```unc_set_type```: indicator of uncertainty set type (interval or budgeted) 
+- ```opt_params```: method-specific parameters (e.g. step size for gradients; tree depth for optimal tree) 
+- ```BASELINE_POL```: ctrl_p_1_mt, 
+-```type```:'IPW'
+
+Performance Evaluation 
+- For multiple treatments: ```oracle_risk``` is a function that takes in the ``` n x k ``` matrix of policy assignment probabilities (robust, and baseline), integer-coded treatments. ```oracle_risk``` sums over treatment partitions. 
+
+## Algorithms for optimizing Policies