balance Quickstart (CBPS): 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]:
%matplotlib inline
import plotly.offline as offline
offline.init_notebook_mode()
from balance import load_data
INFO (2026-05-20 13:28:22,466) [__init__/<module> (line 76)]: Using balance version 0.20.0
INFO (2026-05-20 13:28:22,467) [__init__/<module> (line 81)]:
balance (Version 0.20.0) loaded:
📖 Documentation: https://import-balance.org/
🛠️ Help / Issues: https://github.com/facebookresearch/balance/issues/
📄 Citation:
Sarig, T., Galili, T., & Eilat, R. (2023).
balance - a Python package for balancing biased data samples.
https://arxiv.org/abs/2307.06024
Tip: You can view this message anytime with balance.help()
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"])
target = Sample.from_frame(target_df, outcome_columns=["happiness"])
WARNING (2026-05-20 13:28:22,510) [input_validation/guess_id_column (line 336)]: Guessed id column name id for the data
WARNING (2026-05-20 13:28:22,521) [sample_frame/from_frame (line 327)]: No weights passed. Adding a 'weight' column and setting all values to 1
WARNING (2026-05-20 13:28:22,523) [input_validation/guess_id_column (line 336)]: Guessed id column name id for the data
WARNING (2026-05-20 13:28:22,538) [sample_frame/from_frame (line 327)]: 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.DataFrame'> RangeIndex: 1000 entries, 0 to 999 Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 1000 non-null str 1 gender 912 non-null str 2 age_group 1000 non-null str 3 income 1000 non-null float64 4 happiness 1000 non-null float64 5 weight 1000 non-null float64 dtypes: float64(3), str(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 qqplots) 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()
Adjusting Sample to Population (ipw and cbps)¶
Next, we adjust the sample to the target. The default method to be used is 'ipw' (which uses inverse probability/propensity weights, after running logistic regression with lasso regularization).
In [15]:
# Using ipw to fit survey weights
adjusted_ipw = sample_with_target.adjust()
INFO (2026-05-20 13:28:23,266) [ipw/ipw (line 724)]: Starting ipw function
INFO (2026-05-20 13:28:23,269) [adjustment/apply_transformations (line 434)]: Adding the variables: []
INFO (2026-05-20 13:28:23,271) [adjustment/apply_transformations (line 435)]: Transforming the variables: ['gender', 'age_group', 'income']
INFO (2026-05-20 13:28:23,281) [adjustment/apply_transformations (line 471)]: Final variables in output: ['gender', 'age_group', 'income']
INFO (2026-05-20 13:28:23,404) [ipw/ipw (line 799)]: Building model matrix
INFO (2026-05-20 13:28:23,404) [ipw/ipw (line 800)]: The formula used to build the model matrix: ['income + gender + age_group + _is_na_gender']
INFO (2026-05-20 13:28:23,405) [ipw/ipw (line 801)]: The number of columns in the model matrix: 16
INFO (2026-05-20 13:28:23,406) [ipw/ipw (line 802)]: The number of rows in the model matrix: 11000
INFO (2026-05-20 13:28:40,661) [ipw/ipw (line 998)]: Done with sklearn
INFO (2026-05-20 13:28:40,662) [ipw/ipw (line 1000)]: max_de: None
INFO (2026-05-20 13:28:40,663) [ipw/ipw (line 1022)]: Starting model selection
INFO (2026-05-20 13:28:40,666) [ipw/ipw (line 1078)]: Chosen lambda: 0.041158338186664825
INFO (2026-05-20 13:28:40,666) [ipw/ipw (line 1095)]: Proportion null deviance explained 0.172637976731583
In [16]:
adjusted_cbps = sample_with_target.adjust(method = "cbps")
INFO (2026-05-20 13:28:40,683) [cbps/cbps (line 535)]: Starting cbps function
INFO (2026-05-20 13:28:40,686) [adjustment/apply_transformations (line 434)]: Adding the variables: []
INFO (2026-05-20 13:28:40,687) [adjustment/apply_transformations (line 435)]: Transforming the variables: ['gender', 'age_group', 'income']
INFO (2026-05-20 13:28:40,697) [adjustment/apply_transformations (line 471)]: Final variables in output: ['gender', 'age_group', 'income']
INFO (2026-05-20 13:28:40,843) [cbps/cbps (line 587)]: The formula used to build the model matrix: ['income + gender + age_group + _is_na_gender']
INFO (2026-05-20 13:28:40,844) [cbps/cbps (line 598)]: The number of columns in the model matrix: 16
INFO (2026-05-20 13:28:40,845) [cbps/cbps (line 599)]: The number of rows in the model matrix: 11000
INFO (2026-05-20 13:28:40,851) [cbps/cbps (line 668)]: Finding initial estimator for GMM optimization
INFO (2026-05-20 13:28:41,023) [cbps/cbps (line 695)]: Finding initial estimator for GMM optimization that minimizes the balance loss
INFO (2026-05-20 13:28:42,509) [cbps/cbps (line 731)]: Running GMM optimization
INFO (2026-05-20 13:28:44,117) [cbps/cbps (line 882)]: Done cbps function
In [17]:
print(adjusted_ipw)
Adjusted balance Sample object with target set using ipw
1000 observations x 3 variables: gender,age_group,income,weight_pre_adjust,weight_adjusted_1
id_column: id, weight_column: weight,
outcome_columns: happiness
adjustment details:
method: ipw
weight trimming mean ratio: 20
design effect (Deff): 1.880
effective sample size proportion (ESSP): 0.532
effective sample size (ESS): 531.9
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
In [18]:
# the adjusted object will look the same as ipw
print(adjusted_cbps)
Adjusted balance Sample object with target set using cbps
1000 observations x 3 variables: gender,age_group,income,weight_pre_adjust,weight_adjusted_1
id_column: id, weight_column: weight,
outcome_columns: happiness
adjustment details:
method: cbps
design effect (Deff): 2.754
effective sample size proportion (ESSP): 0.363
effective sample size (ESS): 363.1
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
Evaluation of the Results (CBPS vs IPW)¶
We can get a basic summary of the results:
In [19]:
print(adjusted_ipw.summary())
Adjustment details:
method: ipw
weight trimming mean ratio: 20
Covariate diagnostics:
Covar ASMD reduction: 63.4%
Covar ASMD (7 variables): 0.327 -> 0.120
Covar mean KLD reduction: 92.3%
Covar mean KLD (3 variables): 0.157 -> 0.012
Weight diagnostics:
design effect (Deff): 1.880
effective sample size proportion (ESSP): 0.532
effective sample size (ESS): 531.9
Outcome weighted means:
happiness
source
self 53.295
target 56.278
unadjusted 48.559
Model performance: Model proportion deviance explained: 0.173
In [20]:
print(adjusted_cbps.summary())
Adjustment details:
method: cbps
Covariate diagnostics:
Covar ASMD reduction: 77.4%
Covar ASMD (7 variables): 0.327 -> 0.074
Covar mean KLD reduction: 98.2%
Covar mean KLD (3 variables): 0.157 -> 0.003
Weight diagnostics:
design effect (Deff): 2.754
effective sample size proportion (ESSP): 0.363
effective sample size (ESS): 363.1
Outcome weighted means:
happiness
source
self 54.366
target 56.278
unadjusted 48.559
We can see that CBPS did a better job in terms of ASMD reduction. Let's look at it per feature:
We see an improvement in the average ASMD. We can look at detailed list of ASMD values per variables using the following call.
In [21]:
print("ipw:")
print(adjusted_ipw.covars().asmd().T)
print("\ncbps:")
print(adjusted_cbps.covars().asmd().T)
ipw: source self unadjusted unadjusted - self age_group[T.25-34] 0.021777 0.005688 -0.016090 age_group[T.35-44] 0.055884 0.312711 0.256827 age_group[T.45+] 0.169816 0.378828 0.209013 gender[Female] 0.097916 0.375699 0.277783 gender[Male] 0.103989 0.379314 0.275324 gender[_NA] 0.010578 0.006296 -0.004282 income 0.205469 0.494217 0.288748 mean(asmd) 0.119597 0.326799 0.207202 cbps:
source self unadjusted unadjusted - self age_group[T.25-34] 0.056633 0.005688 -0.050945 age_group[T.35-44] 0.028616 0.312711 0.284095 age_group[T.45+] 0.107172 0.378828 0.271656 gender[Female] 0.034850 0.375699 0.340849 gender[Male] 0.057813 0.379314 0.321500 gender[_NA] 0.039998 0.006296 -0.033702 income 0.113018 0.494217 0.381199 mean(asmd) 0.073793 0.326799 0.253006
It's easier to learn about the biases by just running .covars().plot() on our adjusted object.
In [22]:
adjusted_ipw.covars().plot(library = "seaborn", dist_type = "kde")