balance Quickstart: Analyzing and adjusting the bias on a simulated toy dataset¶

'balance' is a Python package that is maintained and released by the Core Data Science Tel-Aviv team in Meta. 'balance' performs and evaluates bias reduction by weighting for a broad set of experimental and observational use cases.

Although balance is written in Python, you don't need a deep Python understanding to use it. In fact, you can just use this notebook, load your data, change some variables and re-run the notebook and produce your own weights!

This quickstart demonstrates re-weighting specific simulated data, but if you have a different usecase or want more comprehensive documentation, you can check out the comprehensive balance tutorial.

Analysis¶

There are four main steps to analysis with balance:

  • load data
  • check diagnostics before adjustment
  • perform adjustment + check diagnostics
  • output results

Let's dive right in!

Example dataset¶

The following is a toy simulated dataset.

In [1]:
import warnings
warnings.filterwarnings("ignore")

from balance import load_data

INFO (2025-01-07 10:14:49,345) [__init__/<module> (line 54)]: Using balance version 0.10.0
In [2]:
target_df, sample_df = load_data()

print("target_df: \n", target_df.head())
print("sample_df: \n", sample_df.head())

target_df: 
        id gender age_group     income  happiness
0  100000   Male       45+  10.183951  61.706333
1  100001   Male       45+   6.036858  79.123670
2  100002   Male     35-44   5.226629  44.206949
3  100003    NaN       45+   5.752147  83.985716
4  100004    NaN     25-34   4.837484  49.339713
sample_df: 
   id  gender age_group     income  happiness
0  0    Male     25-34   6.428659  26.043029
1  1  Female     18-24   9.940280  66.885485
2  2    Male     18-24   2.673623  37.091922
3  3     NaN     18-24  10.550308  49.394050
4  4     NaN     18-24   2.689994  72.304208
In [3]:
target_df.head().round(2).to_dict()
# sample_df.shape
Out[3]:
{'id': {0: '100000', 1: '100001', 2: '100002', 3: '100003', 4: '100004'},
 'gender': {0: 'Male', 1: 'Male', 2: 'Male', 3: nan, 4: nan},
 'age_group': {0: '45+', 1: '45+', 2: '35-44', 3: '45+', 4: '25-34'},
 'income': {0: 10.18, 1: 6.04, 2: 5.23, 3: 5.75, 4: 4.84},
 'happiness': {0: 61.71, 1: 79.12, 2: 44.21, 3: 83.99, 4: 49.34}}

In practice, one can use pandas loading function(such as read_csv()) to import data into the DataFrame objects sample_df and target_df.

Load data into a Sample object¶

The first thing to do is to import the Sample class from balance. All of the data we're going to be working with, sample or population, will be stored in objects of the Sample class.

In [4]:
from balance import Sample

Using the Sample class, we can fill it with a "sample" we want to adjust, and also a "target" we want to adjust towards.

We turn the two input pandas DataFrame objects we created (or loaded) into a balance.Sample objects, by using the .from_frame()

In [5]:
sample = Sample.from_frame(sample_df, outcome_columns=["happiness"])
# Often times we don'y have the outcome for the target. In this case we've added it just to validate later that the weights indeed help us reduce the bias
target = Sample.from_frame(target_df, outcome_columns=["happiness"])

WARNING (2025-01-07 10:14:49,597) [util/guess_id_column (line 113)]: Guessed id column name id for the data

WARNING (2025-01-07 10:14:49,606) [sample_class/from_frame (line 261)]: No weights passed. Adding a 'weight' column and setting all values to 1

WARNING (2025-01-07 10:14:49,616) [util/guess_id_column (line 113)]: Guessed id column name id for the data

WARNING (2025-01-07 10:14:49,630) [sample_class/from_frame (line 261)]: No weights passed. Adding a 'weight' column and setting all values to 1

If we use the .df property call, we can see the DataFrame stored in sample. We can see how we have a new weight column that was added (it will all have 1s) in the importing of the DataFrames into a balance.Sample object.

In [6]:
sample.df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   id         1000 non-null   object 
 1   gender     912 non-null    object 
 2   age_group  1000 non-null   object 
 3   income     1000 non-null   float64
 4   happiness  1000 non-null   float64
 5   weight     1000 non-null   int64  
dtypes: float64(2), int64(1), object(3)
memory usage: 47.0+ KB

We can get a quick overview text of each Sample object, but just calling it.

Let's take a look at what this produces:

In [7]:
sample
Out[7]:
(balance.sample_class.Sample)

        balance Sample object
        1000 observations x 3 variables: gender,age_group,income
        id_column: id, weight_column: weight,
        outcome_columns: happiness
In [8]:
target
Out[8]:
(balance.sample_class.Sample)

        balance Sample object
        10000 observations x 3 variables: gender,age_group,income
        id_column: id, weight_column: weight,
        outcome_columns: happiness

Next, we combine the sample object with the target object. This is what will allow us to adjust the sample to the target.

In [9]:
sample_with_target = sample.set_target(target)

Looking on sample_with_target now, it has the target atteched:

In [10]:
sample_with_target
Out[10]:
(balance.sample_class.Sample)

        balance Sample object with target set
        1000 observations x 3 variables: gender,age_group,income
        id_column: id, weight_column: weight,
        outcome_columns: happiness
        
            target:
                 
	        balance Sample object
	        10000 observations x 3 variables: gender,age_group,income
	        id_column: id, weight_column: weight,
	        outcome_columns: happiness
	        
            3 common variables: gender,age_group,income

Pre-Adjustment Diagnostics¶

We can use .covars() and then followup with .mean() and .plot() (barplots and kde density plots) to get some basic diagnostics on what we got.

We can see how:

  • The proportion of missing values in gender is similar in sample and target.
  • We have younger people in the sample as compared to the target.
  • We have more females than males in the sample, as compared to around 50-50 split for the (non NA) target.
  • Income is more right skewed in the target as compared to the sample.
In [11]:
print(sample_with_target.covars().mean().T)

source                     self     target
_is_na_gender[T.True]  0.088000   0.089800
age_group[T.25-34]     0.300000   0.297400
age_group[T.35-44]     0.156000   0.299200
age_group[T.45+]       0.053000   0.206300
gender[Female]         0.268000   0.455100
gender[Male]           0.644000   0.455100
gender[_NA]            0.088000   0.089800
income                 6.297302  12.737608
In [12]:
print(sample_with_target.covars().asmd().T)

source                  self
age_group[T.25-34]  0.005688
age_group[T.35-44]  0.312711
age_group[T.45+]    0.378828
gender[Female]      0.375699
gender[Male]        0.379314
gender[_NA]         0.006296
income              0.494217
mean(asmd)          0.326799
In [13]:
print(sample_with_target.covars().asmd(aggregate_by_main_covar = True).T)

source          self
age_group   0.232409
gender      0.253769
income      0.494217
mean(asmd)  0.326799
In [14]:
sample_with_target.covars().plot()