TreatmentPanels
Julia package that defines structs to hold panel data for use in causal inference estimation. This
package is used in the package SynthControl
to provide
input data types for different synthetic control algorithms.
Installation
The package is registered in the General Registry and can be added via
julia> Pkg.add("TreatmentPanels")
or in the Pkg REPL
(@v1.7) add TreatmentPanels
Quickstart
The basic idea of the package is to combine a DataFrame
with a specification of treatment
assignment to construct a an outcome matrix Y
, and an accompanying treatment matrix W
. Both Y
and W
follow the convention that a row is an observational unit and a column is a time period -
i.e. it can be seen as a "wide" panel data set.
julia> using DataFrames, TreatmentPanels
julia> data = DataFrame(region = repeat(["A", "B"], inner = 5), year = repeat(Date(2000):Year(1):Date(2004), 2), outcome = rand(10))
10×3 DataFrame
Row │ region year outcome
│ String Date Float64
─────┼───────────────────────────────
1 │ A 2000-01-01 0.0605538
2 │ A 2001-01-01 0.820218
3 │ A 2002-01-01 0.533732
4 │ A 2003-01-01 0.144979
5 │ A 2004-01-01 0.353885
6 │ B 2000-01-01 0.65294
7 │ B 2001-01-01 0.353973
8 │ B 2002-01-01 0.683144
9 │ B 2003-01-01 0.477427
10 │ B 2004-01-01 0.702888
julia> bp = BalancedPanel(data, "A" => Date(2003); id_var = "region", t_var = "year", outcome_var = "outcome")
Balanced Panel - single unit, single continuous treatment
Treated unit: A
Number of untreated units: 1
First treatment period: 2003-01-01
Number of pretreatment periods: 3
Number of treatment periods: 2
julia> bp.Y
2×5 Matrix{Float64}:
0.0605538 0.820218 0.533732 0.144979 0.353885
0.65294 0.353973 0.683144 0.477427 0.702888
julia> bp.W
2×5 Matrix{Bool}:
0 0 0 1 1
0 0 0 0 0
The package provides simple plotting functionality to visualise treatment assignment and outcomes:
julia> using Plots
julia> plot(bp, markersize = 10)
julia> plot(bp; kind = "outcome")
Types provided
There are two basic types:
-
BalancedPanel{T1, T2} <: TreatmentPanel
holds panel data in which each unit of observation$i = 1, ..., N$ is observed for$t = 1, ..., T$ periods -
UnbalancedPanel{T1, T2}
holds panel data in which there might be a different number of periods for each unit of observation
Basic usage of the BalancedPanel
constructor is
BalancedPanel(data::DataFrame, treatment_assignment; id_var, t_var, outcome_var)::BalancedPanel{T1, T2}
where
-
data
is a DataFrame holding the panel data, including columns holdingid_var
,t_var
, andoutcome_var
-
treatment_assignment
specifies the units treated and timing of treatment. -
id_var
is the name of the column indata
holding the unit identifier -
t_var
is the name of the column indata
holding the time identifier -
outcome_var
is the name of the column indata
holding data on the outcome variable of interest
T1 <: UnitTreatmentType
and T2 <: TreatmentDurationType
are types indicating the nature the
treatment observed. UnitTreatmentType
currently has three concrete types:
-
SingleUnitTreatment
- only one unit of observation is subject to treatment -
MultiUnitSimultaneousTreatment
- multiple units are being treated in the same time period -
MultiUnitStaggeredTreatment
- multiple units are being treated in different periods
TreatmentDurationType
currently has two concrete types:
-
ContinuousTreatment
- once a unit is treated, it remains treated for the remainder of the observation period -
StartEndTreatment
- the treatment has an end point in time, i.e. treated units revert to being untreated at some point
The types of T1
and T2
are automatically chosen based on the treatment_assignment
passed.
Only starting point | Start and end point | Multiple start & end points | |
---|---|---|---|
one unit | Pair{String, Date} | Pair{String, Tuple{Date, Date}} | Pair{String}, Vector{Tuple{Date, Date}}} |
multiple units | Vector{Pair{String, Date}} | Vector{Pair{String, Tuple{Date, Date}}} | Vector{Pair{String, Vector{Tuple{Date, Date}}}} |
As an example, calling BalancedPanel(data, "unit1" => Date(2000))
will return an object of type
BalancedPanel{SingleUnitTreatment, ContinuousTreatment}
, as only one unit is treated and only a
start point for treatment is provided, so it is assumed that all periods from the year 2000 onwards
are treatment periods. BalancedPanel(data, "unit1" => (Date(2000) => Date(2010))
in contrast will
return a BalancedPanel{SingleUnitTreatment, StartEndTreatment}
, and any observations in the year
2011 or after will be considered untreated.
While many of the convenience methods in this package are specialised on the concrete types of T1
and T2
, their main role is in supporting dispatch for synthetic control models. As an example, a
standard synthetic control model is designed to estimate the impact of a continuous treatment for a
single unit, and hence should accept a BalancedPanel{SingleUnitTreatment, ContinuousTreatment}
as
input, while a staggered synthetic control model will accept a
BalancedPanel{MultiUnitStaggeredTreatment, T2}
. In this way, the type of the BalancedPanel
passed to an estimation routine can be used to enforce the data structure assumed in the routine.