Update CausalInference.md

New organisation for the policy learning section

Author: julierennes

CausalInference.md

@@ -214,41 +214,21 @@ treatment effect (HTE) estimation.
 
 ### [Policy learning and dynamic treatment regimes]{#policy}
 
--   *Estimation of an optimal dynamic treatment regime (DTR)* is
-    implemented in `r pkg("DynTxRegime")` (Q-Learning, Interactive Q-Learning,
-    weighted learning, and value-search methods based on Augmented Inverse
-    Probability Weighted Estimators and Inverse Probability Weighted
-    Estimators); methods based on
-    marginal quantile, marginal mean, and mean absolute difference are
-    implemented in `r pkg("quantoptr")` as well as doubly-robust methods for
-    quantile-optimal treatment regime). `r pkg("lmtp")` also provides doubly-robust
-    causal effect estimates for modified treatment policies, dynamic treatment regimes (and static interventions). `r pkg("DTRreg")` proposes different
-    methods such as G-estimation, dynamic weighted OLS and Q-learning, as well
-    as several variance estimation approaches, it can handle survival
-    outcomes and continuous treatment variables, while `r pkg("DTRKernSmooth")` uses kernel smoothing to examine optimal linear regimes. `r pkg("QTOCen")` provides
-    methods for estimation of mean- and quantile-optimal treatment regimes
-    from censored data. `r pkg("simml")` and `r pkg("simsl")` offer
-    Single-Index Models with Multiple-Links for, respectively, experimental
-    and observational data. `r pkg("personalized")` implements methods for
-    estimation of individualized treatment rules from observational and
-    randomized data with options for variable-selection and gradient boosting
-    based estimation, and for outcome model augmentation (for continuous,
-    binary, count, and time-to-event outcomes).  `r pkg("polle")` provides a unified framework for
-    learning and evaluating finite stage policies based on observational data
-    with methods such as doubly robust restricted Q-learning, policy tree
-    learning,  and outcome weighted learning. Flexible machine learning methods
-    can be used to estimate the nuisance components and valid inference for the 
-    policy value is ensured via cross-fitting.
--   `r pkg("OTRselect")` implements a penalized
-    regression method that can simultaneously estimate the optimal
-    treatment strategy and identify important variables for either
-    censored or uncensored continuous response. `r pkg("DTRlearn2")` offers
-    Q-learning and outcome-weighted learning methods with variable selection
-    via penalization.
--   *For sequential, multiple assignment, randomized trials (SMART)*,
-    `r pkg("smartsizer")` provides a set of tools for determining the necessary
-    sample size in order to identify the optimal DTR; `r pkg("DTRlearn2")` also
-    implements estimators for general K-stage DTRs from SMARTs.
+*Direct methods* perform a direct classification task: outcome-weighted learning `r pkg("DTRlearn2")`; efficient augmentation/relaxation learning (EARL), residual learning, weighted learning, value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators `r pkg("DynTxRegime")`.
+*Indirect methods*, which proceed through nuisance estimation to perform Q-learning `r pkg("DTRlearn2")`, `r pkg("DynTxRegime")`, `r pkg("DTRreg")`, Interactive Q-Learning `r pkg("DynTxRegime")` or double robust Q-Learning.
+r pkg("polle") provides a unified framework for learning and evaluating finite stage policies based on observational data with methods such as doubly robust restricted Q-learning, policy tree learning, and outcome weighted learning. Flexible machine learning methods can be used to estimate the nuisance components and valid inference for the policy value is ensured via cross-fitting. The package wraps and extends some functionalities from other packages  `r pkg("DynTxRegime") `,  `r pkg("policytree") `,  `r pkg("grf") `,  `r pkg("DTRlearn2"))`.
+
+*For sequential, multiple assignment, randomized trials (SMART)*
+Set of tools for determining the necessary sample size in order to identify the optimal DTR `r pkg("smartsizer")`. Estimators for general K-stage DTRs from SMARTs `r pkg("DTRlearn2")`.
+
+*With variable selection*
+Outcome weighted learning with variable selection via penalization `r pkg("DTRlearn2")`. Estimation of individualized treatment rules from observational and randomized data with options for variable-selection and gradient boosting based estimation, and for outcome model augmentation for continuous, binary, count, and time-to-event outcomes `r pkg("personalized")`. Penalized regression that can simultaneously estimate the optimal treatment strategy and identify important variables for either censored or uncensored continuous response `r pkg("OTRselect")`.
+
+*Other methods using quantiles*:
+Marginal quantile, marginal mean, and mean absolute difference and doubly-robust methods for quantile-optimal treatment regime `r pkg("quantoptr")`, estimation of mean- and quantile-optimal treatment regimes from censored data `r pkg("QTOCen")`.
+
+*Other approaches*
+Learn optimal policies via doubly robust empirical welfare maximization over trees `r pkg("policytree")`. Doubly-robust causal effect estimated for modified treatment policies, dynamic treatment regimes and static interventions `r pkg("lmtp")`. G-estimation, dynamic weighted OLS, several variance estimation approaches with possibilities to handle survival outcomes and continuous treatment variables `r pkg("DTRreg")`. Methods using kernel smoothing to examine optimal linear regimes `r pkg("DTRKernSmooth")`. Single-Index Models with Multiple-Links for, respectively, experimental and observational data `r pkg("simml")`, `r pkg("simsl")`
 
 
 ### [Structural equation models (SEM), do-calculus causal discovery]{#dag}