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| 1 | +## CRE 0.1.0 (2022-10-17) |
| 2 | + |
| 3 | +### Changed |
| 4 | + |
| 5 | +* `select_causal_rules()` is now `lasso_rules_filter()` |
| 6 | +* rules generation now accepts replace parameter to set replacement in bootstrapping |
| 7 | +* rename parameter `t` with `t_anom` |
| 8 | +* add parameter `t_corr` discard correlation threshold |
| 9 | +* define `discard_anomalous_rules()` and `discard_corre_rules()` functions and |
| 10 | +and relative tests |
| 11 | +* reorganize `generate_rules_matrix()` (separate standardization, and remove filtering) |
| 12 | +* explicit `prune_rules()` function and add relative tests |
| 13 | +* remove `take1()` function for random Rule Selection |
| 14 | +* add effect modifiers filter for Rule Generation |
| 15 | +* add `generate_causal_rules()` function and relative tests |
| 16 | +* solve Undesired 'All' Decision Rule Issue |
| 17 | +* solve No Causal Rule Selected Issue |
| 18 | +* improve `cre.summary()` function |
| 19 | +* `min_nodes` --> `node_size` (following the randomForest convention) |
| 20 | +* `estimate_cate` include five methods for estimating the CATE values (`poisson`, `DRLearner`, `bart-baggr`, `cf-means`, `linreg`) |
| 21 | +* `cre` added new arguments to (1) complement `SuperLearner` package (`ps_method_dis`, `ps_method_inf`, `or_method_dis`, `or_method_inf`, `cate_SL_library`) and to (2) select CATE method and (3) whether to filter CATE p-values (`cate_method` and `filter_cate`). |
| 22 | +Now returns an S3 object. |
| 23 | +* `estimate_ite_xyz` conduct propensity score estimation using helper function with `SuperLearner` package |
| 24 | +* `generate_cre_dataset` make number of covariates an argument of the function |
| 25 | +* improve examples and update tests for all functions |
| 26 | + |
| 27 | + |
| 28 | +### Added |
| 29 | +* `print` and `summary` generic functions. |
| 30 | +* `check_input` function to isolate input checks. |
| 31 | +* `estimate_ite_aipw` function for augmented inverse propensity weighting |
| 32 | +* `plot.cre` generic function to plot CRE S3 object Results |
| 33 | +* `test-cre_functional.R` tests the functionality of the package |
| 34 | +* `stability_selection` function for causal rules selection |
| 35 | + |
| 36 | +### Removed |
| 37 | + |
| 38 | +* `estimate_ite_blp` function |
| 39 | + |
1 | 40 | ## CRE 0.0.1 (2021-10-20) |
2 | 41 |
|
3 | 42 | ### Changed |
4 | | -- `estimate_cate` include two methods for estimating the CATE values |
5 | | -- `cre` added initial checks for binary outcome and whether to include the propensity score in the ITE estimation |
6 | | -- `estimate_ite_xyz` conduct propensity score estimation using helper function |
7 | | -- Removed `seed` as an input from `generate_cre_dataset` function. |
| 43 | +* `estimate_cate` include two methods for estimating the CATE values |
| 44 | +* `cre` added initial checks for binary outcome and whether to include the propensity score in the ITE estimation |
| 45 | +* `estimate_ite_xyz` conduct propensity score estimation using helper function |
| 46 | +* Removed `seed` as an input from `generate_cre_dataset` function. |
8 | 47 |
|
9 | 48 | ### Added |
10 | | -- `set_logger` and `get_logger` |
11 | | -- `check_input_data` function |
12 | | -- example to `generate_cre_dataset` |
13 | | -- `generate_cre_dataset` function to generate synthetic data for testing the package |
14 | | -- `test-generate_cre_dataset` function test |
15 | | -- `estimate_ps` function to estimate the propensity score |
16 | | -- `estimate_ite_xbart` function to generate ITE estimates using accelerated BART |
17 | | -- `estimate_ite_xbcf` function to generate ITE estimates using accelerated BCF |
18 | | -- `analyze_sensitivity` function to conduct sensitivity analysis for unmeasured confounding |
19 | | -- `cre` function to perform the entire Causal Rule Ensemble method |
20 | | -- `estimate_cate` function to generate CATE estimates from the ITE estimates and select rules |
21 | | -- `estimate_ite` function to generate ITE estimates using the user-specified method (calls the other `estimate_ite_xyz` functions) |
22 | | -- `estimate_ite_bart` function to generate ITE estimates using BART |
23 | | -- `estimate_ite_bcf` function to generate ITE estimates using Bayesian Causal Forests |
24 | | -- `estimate_ite_cf` function to generate ITE estimates using Causal Forests |
25 | | -- `estimate_ite_ipw` function to generate ITE estimates using IPW |
26 | | -- `estimate_ite_or` function to generate ITE estimates using Outcome Regression |
27 | | -- `estimate_ite_sipw` function to generate ITE estimates using SIPW |
28 | | -- `extract_rules` function to extract a list of causal rules from randomForest and GBM models |
29 | | -- `generate_rules` function to generate causal rule models using randomForest and GBM methods |
30 | | -- `generate_rules_matrix` function to convert a list of causal rules into a matrix |
31 | | -- `select_causal_rules` function to apply penalized regression to causal rules to select only the most important ones |
32 | | -- `split_data` function to split input data into discovery and inference subsamples |
33 | | -- `take1` function to create a subsample of indices |
| 49 | +* `set_logger` and `get_logger` |
| 50 | +* `check_input_data` function |
| 51 | +* example to `generate_cre_dataset` |
| 52 | +* `generate_cre_dataset` function to generate synthetic data for testing the package |
| 53 | +* `test-generate_cre_dataset` function test |
| 54 | +* `estimate_ps` function to estimate the propensity score |
| 55 | +* `estimate_ite_xbart` function to generate ITE estimates using accelerated BART |
| 56 | +* `estimate_ite_xbcf` function to generate ITE estimates using accelerated BCF |
| 57 | +* `analyze_sensitivity` function to conduct sensitivity analysis for unmeasured confounding |
| 58 | +* `cre` function to perform the entire Causal Rule Ensemble method |
| 59 | +* `estimate_cate` function to generate CATE estimates from the ITE estimates and select rules |
| 60 | +* `estimate_ite` function to generate ITE estimates using the user-specified method (calls the other `estimate_ite_xyz` functions) |
| 61 | +* `estimate_ite_bart` function to generate ITE estimates using BART |
| 62 | +* `estimate_ite_bcf` function to generate ITE estimates using Bayesian Causal Forests |
| 63 | +* `estimate_ite_cf` function to generate ITE estimates using Causal Forests |
| 64 | +* `estimate_ite_ipw` function to generate ITE estimates using IPW |
| 65 | +* `estimate_ite_or` function to generate ITE estimates using Outcome Regression |
| 66 | +* `estimate_ite_sipw` function to generate ITE estimates using SIPW |
| 67 | +* `extract_rules` function to extract a list of causal rules from randomForest and GBM models |
| 68 | +* `generate_rules` function to generate causal rule models using randomForest and GBM methods |
| 69 | +* `generate_rules_matrix` function to convert a list of causal rules into a matrix |
| 70 | +* `select_causal_rules` function to apply penalized regression to causal rules to select only the most important ones |
| 71 | +* `split_data` function to split input data into discovery and inference subsamples |
| 72 | +* `take1` function to create a subsample of indices |
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