README.Rmd

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## FLAME (Fast, Large-scale, Almost Matching Exactly)

### Overview of the FLAME algorithm 

The **FLAME** algorithm provides fast and large-scale matching approach to causal inference. **FLAME** creates matches that include as many covariates as possible, and iteratively drops covariates that are successively less useful for predicting outcomes based on matching quality. Currently the `FLAME` package applies to categorical data, and provides two approaches for implementation - bit vectors and database management systems (e.g., PostgreSQL, SQLite). For data that has been preprocessed and fits in memory, bit vectors should be applied. For extremely large data that does not fit in memory, database systems should be applied.

For more details about the FLAME algorithm, please refer to the paper: [FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference ](https://arxiv.org/pdf/1707.06315.pdf) and [FLAME: An R Package for a Fast Large-scale Almost Matching Exactly Approach to Causal Inference](articles/jss.html)

### Installation  

```{r, eval=FALSE}
# Download from CRAN
install.packages("FLAME")

# Download from GitHub
devtools::install_github("chiarui424/FLAME") 
```

### Required data format

`FLAME` package requires input data to have specific format. First, input data should be a **R Data Frame**. Second, all covariates in the input data should be categorical covariates, represented by *factor* R data type. If there are continuous covariates, please consider regrouping. Third, input data columns should contain (1) covariates in *factor* data type, (2) outcome variable in *numeric* data type, and (3) variable specifying a unit is treated or control (treated = 1, control = 0) in *factor* data type. Lastly, though there are no requirements for input data column names, the column order should follow *[covariates, outcome, treated]*. Below is an example of input data with $n$ units and $m$ covariates.


|              | **$x_1$** | **$x_2$** | **...** | **$x_{m-1}$** | **$x_m$** | **outcome** | **treated** |
| :---:        | :---:     | :---:     | :---:   | :---:         | :---:     | :---:       | :---:       | 
| **R data type**| *factor*| *factor*  | *factor*| *factor*      | *factor*  | *numeric*   | *factor*    |
| **unit 1**   | 0         | 1         | ...     | 1             | 2         | 3.8         | 1           |
| **unit 2**   | 1         | 0         | ...     | 1             | 0         | 1.36        | 0           |
| **unit 3**   | 0         | 1         | ...     | 0             | 1         | -7.25       | 0           |
| ...          | ...       | ...       | ...     | ...           | ...       | ...         | ...         |
| **unit $n$** | 0         | 1         | ...     | 1             | 0         | 20          | 1           |


Holdout training set should also follow the same format.

### Other requirements

1. `FLAME` requires installation of python, specifically with at least python 2.7 version. If your computer system does not have python 2.7, install from [here](https://www.python.org/downloads/). 

2. For database systems implementation, FLAME package provides two versions - SQLite and PostgreSQL. PostgreSQL requires installation of external database system but it is faster. SQLite does not require external database system but is slower. If your computer does not have PostgreSQL installed, install from [here](https://www.postgresql.org/download/). For connecting and setup of PostgreSQL server, please refer to the [tutorial]( at \url{)http://www.postgresqltutorial.com/connect-to-postgresql-database/)

3. For database systems implementation, please name the database connection as **db**.

### Some examples 

[Apply the FLAME Algorithm to Synthetic Data](articles/Synthetic_Data.html)   


[Get Detailed Information After Matching](articles/CATE.html)  


[Match Quality](articles/Match_Quality.html)