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 (2025-05-23 17:44:01,893) [__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 (2025-05-23 17:44:02,207) [util/guess_id_column (line 113)]: Guessed id column name id for the data
WARNING (2025-05-23 17:44:02,216) [sample_class/from_frame (line 261)]: No weights passed. Adding a 'weight' column and setting all values to 1
WARNING (2025-05-23 17:44:02,227) [util/guess_id_column (line 113)]: Guessed id column name id for the data
WARNING (2025-05-23 17:44:02,242) [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()
Adjusting Sample to Population¶
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 = sample_with_target.adjust()
INFO (2025-05-23 17:44:03,123) [ipw/ipw (line 399)]: Starting ipw function
INFO (2025-05-23 17:44:03,127) [adjustment/apply_transformations (line 305)]: Adding the variables: []
INFO (2025-05-23 17:44:03,128) [adjustment/apply_transformations (line 306)]: Transforming the variables: ['gender', 'age_group', 'income']
INFO (2025-05-23 17:44:03,142) [adjustment/apply_transformations (line 343)]: Final variables in output: ['gender', 'age_group', 'income']
INFO (2025-05-23 17:44:03,153) [ipw/ipw (line 433)]: Building model matrix
INFO (2025-05-23 17:44:03,252) [ipw/ipw (line 455)]: The formula used to build the model matrix: ['income + gender + age_group + _is_na_gender']
INFO (2025-05-23 17:44:03,253) [ipw/ipw (line 458)]: The number of columns in the model matrix: 16
INFO (2025-05-23 17:44:03,253) [ipw/ipw (line 459)]: The number of rows in the model matrix: 11000
INFO (2025-05-23 17:44:19,814) [ipw/ipw (line 578)]: Done with sklearn
INFO (2025-05-23 17:44:19,814) [ipw/ipw (line 584)]: max_de: None
INFO (2025-05-23 17:44:19,815) [ipw/ipw (line 605)]: Starting model selection
INFO (2025-05-23 17:44:19,818) [ipw/ipw (line 638)]: Chosen lambda: 0.041158338186664825
INFO (2025-05-23 17:44:19,819) [ipw/ipw (line 654)]: Proportion null deviance explained 0.17265121909892267
In [16]:
print(adjusted)
Adjusted balance Sample object with target set using ipw
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
Evaluation of the Results¶
We can get a basic summary of the results:
In [17]:
print(adjusted.summary())
Covar ASMD reduction: 63.4%, design effect: 1.880 Covar ASMD (7 variables): 0.327 -> 0.119 Model performance: Model proportion deviance explained: 0.173
In [18]:
print(adjusted.covars().mean().T)
source self target unadjusted _is_na_gender[T.True] 0.086866 0.089800 0.088000 age_group[T.25-34] 0.307309 0.297400 0.300000 age_group[T.35-44] 0.273676 0.299200 0.156000 age_group[T.45+] 0.137604 0.206300 0.053000 gender[Female] 0.406342 0.455100 0.268000 gender[Male] 0.506792 0.455100 0.644000 gender[_NA] 0.086866 0.089800 0.088000 income 10.060502 12.737608 6.297302
We see an improvement in the average ASMD. We can look at detailed list of ASMD values per variables using the following call.
In [19]:
print(adjusted.covars().asmd().T)
source self unadjusted unadjusted - self age_group[T.25-34] 0.021676 0.005688 -0.015988 age_group[T.35-44] 0.055738 0.312711 0.256973 age_group[T.45+] 0.169759 0.378828 0.209069 gender[Female] 0.097907 0.375699 0.277792 gender[Male] 0.103798 0.379314 0.275516 gender[_NA] 0.010260 0.006296 -0.003965 income 0.205436 0.494217 0.288781 mean(asmd) 0.119494 0.326799 0.207304
It's easier to learn about the biases by just running .covars().plot() on our adjusted object.
In [20]:
adjusted.covars().plot() # you could change sizes using something like .plot(width = 1500, height = 700)
We can also use different plots, using the seaborn library, for example with the "kde" dist_type.
In [21]:
# This shows how we could use seaborn to plot a kernel density estimation
adjusted.covars().plot(library = "seaborn", dist_type = "kde")
Understanding the weights¶
We can look at the distribution of weights using the following call.
In [22]:
adjusted.weights().plot()
And get many summary statistics - including the design effect, effective sample size (ESS), and various quantiles and more, using:
In [23]:
# adjusted.weights().design_effect()
print(adjusted.weights().summary().round(2))
var val 0 design_effect 1.88 1 effective_sample_proportion 0.53 2 effective_sample_size 531.79 3 sum 10000.00 4 describe_count 1000.00 5 describe_mean 1.00 6 describe_std 0.94 7 describe_min 0.30 8 describe_25% 0.45 9 describe_50% 0.65 10 describe_75% 1.17 11 describe_max 11.36 12 prop(w < 0.1) 0.00 13 prop(w < 0.2) 0.00 14 prop(w < 0.333) 0.11 15 prop(w < 0.5) 0.32 16 prop(w < 1) 0.67 17 prop(w >= 1) 0.33 18 prop(w >= 2) 0.10 19 prop(w >= 3) 0.03 20 prop(w >= 5) 0.01 21 prop(w >= 10) 0.00 22 nonparametric_skew 0.37 23 weighted_median_breakdown_point 0.21
Outcome analysis¶
In [24]:
# As we can see, the ci for unadjusted doesn't include the real value in the outcome, while the CI of the adjusted sample does include it.
# Also, the distance from the true value without adjustment is around 4 points, and after adjustment it's around 2 points.
print(adjusted.outcomes().summary())
1 outcomes: ['happiness']
Mean outcomes (with 95% confidence intervals):
source self target unadjusted self_ci target_ci unadjusted_ci
happiness 53.297 56.278 48.559 (52.097, 54.496) (55.961, 56.595) (47.669, 49.449)
Response rates (relative to number of respondents in sample):
happiness
n 1000.0
% 100.0
Response rates (relative to notnull rows in the target):
happiness
n 1000.0
% 10.0
Response rates (in the target):
happiness
n 10000.0
% 100.0
The estimated mean happiness according to our sample is 48 without any adjustment and 54 with adjustment. The following show the distribution of happinnes:
In [25]:
adjusted.outcomes().plot()
Downloading data¶
Finally, we can prepare the data to be downloaded for future analyses.
In [26]:
adjusted.to_download()
Out[26]:
Click here to download: /tmp/tmp_balance_out_0e641fa7-7850-42de-8302-a63b89244db4.csv
In [27]:
# We can prepare the data to be exported as csv - showing the first 500 charaacters for simplicity:
adjusted.to_csv()[0:500]
Out[27]:
'id,gender,age_group,income,happiness,weight\n0,Male,25-34,6.428659499046228,26.043028759747298,6.52832077256206\n1,Female,18-24,9.940280228116047,66.88548460632677,9.615962486362896\n2,Male,18-24,2.6736231547518043,37.091921916683006,3.5613441674585165\n3,,18-24,10.550307519418066,49.39405003271002,6.958765976140972\n4,,18-24,2.689993854299385,72.30420755038209,5.1335477016020254\n5,,35-44,5.995497722733131,57.28281646341816,16.44496201550232\n6,,18-24,12.63469573898972,31.663293445944596,8.19816512783'
In [28]:
# Sessions info
import session_info
session_info.show(html=False, dependencies=True)
----- balance 0.10.0 pandas 2.0.3 session_info v1.0.1 ----- PIL 11.2.1 anyio NA arrow 1.3.0 asttokens NA attr 25.3.0 attrs 25.3.0 babel 2.17.0 certifi 2025.04.26 charset_normalizer 3.4.2 comm 0.2.2 cycler 0.12.1 cython_runtime NA dateutil 2.9.0.post0 debugpy 1.8.14 decorator 5.2.1 defusedxml 0.7.1 exceptiongroup 1.3.0 executing 2.2.0 fastjsonschema NA fqdn NA idna 3.10 importlib_metadata NA importlib_resources NA ipfn NA ipykernel 6.29.5 isoduration NA jedi 0.19.2 jinja2 3.1.6 joblib 1.5.1 json5 0.12.0 jsonpointer 3.0.0 jsonschema 4.23.0 jsonschema_specifications NA jupyter_events 0.12.0 jupyter_server 2.16.0 jupyterlab_server 2.27.3 kiwisolver 1.4.7 markupsafe 3.0.2 matplotlib 3.9.4 matplotlib_inline 0.1.7 mpl_toolkits NA narwhals 1.40.0 nbformat 5.10.4 numpy 1.26.4 overrides NA packaging 25.0 parso 0.8.4 patsy 1.0.1 pexpect 4.9.0 platformdirs 4.3.8 plotly 6.1.1 prometheus_client NA prompt_toolkit 3.0.51 psutil 7.0.0 ptyprocess 0.7.0 pure_eval 0.2.3 pydev_ipython NA pydevconsole NA pydevd 3.2.3 pydevd_file_utils NA pydevd_plugins NA pydevd_tracing NA pygments 2.19.1 pyparsing 3.2.3 pythonjsonlogger NA pytz 2025.2 referencing NA requests 2.32.3 rfc3339_validator 0.1.4 rfc3986_validator 0.1.1 rpds NA scipy 1.10.1 seaborn 0.13.2 send2trash NA six 1.17.0 sklearn 1.2.2 sniffio 1.3.1 sphinxcontrib NA stack_data 0.6.3 statsmodels 0.14.4 threadpoolctl 3.6.0 tornado 6.5.1 traitlets 5.14.3 typing_extensions NA uri_template NA urllib3 2.4.0 wcwidth 0.2.13 webcolors NA websocket 1.8.0 yaml 6.0.2 zipp NA zmq 26.4.0 zoneinfo NA ----- IPython 8.18.1 jupyter_client 8.6.3 jupyter_core 5.7.2 jupyterlab 4.4.2 notebook 7.4.2 ----- Python 3.9.22 (main, Apr 8 2025, 21:45:32) [GCC 13.3.0] Linux-6.11.0-1014-azure-x86_64-with-glibc2.39 ----- Session information updated at 2025-05-23 17:44