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https://github.com/grf-labs/policytree
Policy learning via doubly robust empirical welfare maximization over trees
https://github.com/grf-labs/policytree
causal-inference policy-learning
Last synced: 1 day ago
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Policy learning via doubly robust empirical welfare maximization over trees
- Host: GitHub
- URL: https://github.com/grf-labs/policytree
- Owner: grf-labs
- License: mit
- Created: 2019-12-17T18:38:46.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-06-14T01:18:42.000Z (5 months ago)
- Last Synced: 2024-10-28T17:23:45.376Z (14 days ago)
- Topics: causal-inference, policy-learning
- Language: R
- Homepage: https://grf-labs.github.io/policytree/
- Size: 1.52 MB
- Stars: 76
- Watchers: 9
- Forks: 15
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: COPYING
Awesome Lists containing this project
README
# policytree
[](https://cran.r-project.org/package=policytree)
[](https://dev.azure.com/grf-labs/grf/_build/latest?definitionId=1&branchName=master)
A package for learning simple rule-based policies, where the rule takes the form of a shallow decision tree. Applications include settings which require interpretable predictions, such as for example a medical treatment prescription. This package uses doubly robust reward estimates from [grf](https://github.com/grf-labs/grf) to find a shallow, but globally optimal decision tree.
Some helpful links for getting started:
* The [R package documentation](https://grf-labs.github.io/policytree/) contains usage examples and method references.
* For community questions and answers around usage, see the GitHub [issues page](https://github.com/grf-labs/policytree/issues).
* The package [sparse policytree](https://github.com/Yale-Medicaid/sparsepolicytree) uses an alternate solver that may be faster on large datasets with sparse categorical features.
### Installation
The latest release of the package can be installed through CRAN:
```R
install.packages("policytree")
```
To install the latest development version from source:
```R
devtools::install_github("grf-labs/policytree", subdir = "r-package/policytree")
```
Installing from source requires a C++ 11 compiler (on Windows Rtools is required as well) together with the R packages
`Rcpp` and `BH`.
### Multi-action policy learning example
```r
library(policytree)
n <- 250
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- as.factor(sample(c("A", "B", "C"), n, replace = TRUE))
Y <- X[, 1] + X[, 2] * (W == "B") + X[, 3] * (W == "C") + runif(n)
multi.forest <- grf::multi_arm_causal_forest(X, Y, W)
# Compute doubly robust reward estimates.
Gamma.matrix <- double_robust_scores(multi.forest)
head(Gamma.matrix)
# A B C
# 1 -0.002612209 -0.1438422 -0.04243015
# 2 0.417066177 0.4212708 1.04000173
# 3 2.020414370 0.3963890 1.33038496
# 4 1.193587749 1.7862142 -0.05668051
# 5 0.808323778 0.5017521 1.52094053
# 6 -0.045844471 -0.1460745 -1.56055025
# Fit a depth 2 tree on a random training subset.
train <- sample(1:n, 200)
opt.tree <- policy_tree(X[train, ], Gamma.matrix[train, ], depth = 2)
opt.tree
# policy_tree object
# Tree depth: 2
# Actions: 1: A 2: B 3: C
# Variable splits:
# (1) split_variable: X3 split_value: 0.368037
# (2) split_variable: X2 split_value: -0.098143
# (4) * action: 1
# (5) * action: 2
# (3) split_variable: X2 split_value: 1.25697
# (6) * action: 3
# (7) * action: 2
## Predict treatment on held out data
head(predict(opt.tree, X[-train, ]))
#> [1] 2 3 1 2 3 3
```
### Details
`policy_tree()`: fits a depth _k_ tree by exhaustive search (_Nxp_ features on _Nxd_ actions). The optimal tree maximizes the sum of rewards: let $\Gamma_i \in \mathbb R^d$ be a vector of unit-specific rewards for each action 1 to $d$ and $\pi(X_i) \in \\{1, ..., d\\}$ a mapping from covariates $X_i$ to action. `policy_tree` solves the following:
$$
\pi^* = argmax_{\pi \in \Pi} \left[\frac{1}{n} \sum_{i=1}^{n} \Gamma_i(\pi(X_i)) \right],
$$
where $\Pi$ is the class of depth-_k_ decision trees. (`hybrid_policy_tree()` employs a mix between a optimal/greedy approach and can be used to fit deeper trees).
`double_robust_scores()`: computes doubly robust reward estimates for a subset of _grf_ forest types.
### Contributing
Contributions are welcome, please consult the [development guide](https://github.com/grf-labs/policytree/blob/master/DEVELOPING.md) for details.
### Funding
Development of policytree is supported by the National Science Foundation, the Sloan Foundation, the Office of Naval Research (Grant N00014-17-1-2131) and Schmidt Futures.
### References
Susan Athey and Stefan Wager.
Policy Learning With Observational Data. Econometrica 89.1 (2021): 133-161.
[paper,
arxiv]
Toru Kitagawa and Aleksey Tetenov.
Who Should be Treated? Empirical Welfare Maximization Methods for Treatment Choice. Econometrica 86.2 (2018): 591-616.
[paper]
Erik Sverdrup, Ayush Kanodia, Zhengyuan Zhou, Susan Athey, and Stefan Wager.
policytree: Policy learning via doubly robust empirical welfare maximization over trees. Journal of Open Source Software, 5(50), 2020.
[paper]
Zhengyuan Zhou, Susan Athey, and Stefan Wager.
Offline Multi-Action Policy Learning: Generalization and Optimization. Operations Research 71.1 (2023).
[paper,
arxiv]