* updated the method signature and docstrings for estimator classes
* bug fixes
* updated the args passing
* fixed the bug in refutation test calls for num_ci_simulations
* used std init args
* updated num simulations to pass weighting test
* updated docstrings to make sure they are in correct format
* Removed output from notebooks
* updated notebooks to fix any bugs
* updated refuter notebook
* updated format
Earlier, DoWhy would automatically add an unobserved variable, but it created confusion.
New behavior:
DoWhy does not add an unobserved variable. User has to provide it.
Identification proceeds as per user-provided graph. Simplified code.
* proceed_when_unidentifiable is redundant. Will be removed in a future version.
* fixed some identification errors with unobserved variables
* removed unobserved c from default graph of datasets.py
* fixed some notebook errors
* added error messages when identification has failed
* Used the d-separation algorithm from networkx
* Updated backdoor identification to be faster
* Refactor code for the new function
* Changed the logic for unobserved confounding to simply remove the unobserved confounder when returning a backdoor set. Simplifies the logic.
PS Stratification needed users to specify both num_strata and clipping_threshold. This PR adds an iterative method to select num_strata automatically given a clipping_threshold. The thumb rule is that the chosen num_strata should lead to at least half of the strata having at least clipping_threshold data points for both treatment and control.
* automatic stratification
* Updated datasets file to take custom std dev for treatment
* Added more noise in treatment in notebooks to simulate a realistic setting
* Updated getting started notebook
* updated sensitivity plots for add common cause refuter
* updated random common cause to include p-value and better docs for notebook
* updated getting started notebook
Checks if effect_modifiers is [] or None in causal_model.py
Fixes a typo in causal graph in member-rewards notebook
removes a top-level import statement for graphviz
* Added identify_vars and different functions to calculate common causes, instruments and effect modifiers
* Added class for Backdoor variable search
* Check for blocked paths during DFS
* Improved memoization
* Added Hitting Set Algorithm to generate backdoor variables per node pair
* Added test file and other changes
* Added 2 more test cases in pytest file
* Added notebook for optimized backdoor identification
* Added docstring
* Add condition to prevent adding colliders to backdoor paths
* Added code for ID Algorithm
* Replaced set and list by OrderedSet
* Need to prepare recursive datastructure for results
* Added function to utils/graph_operations.py
* Added print in a readable fashion
* Updated ID notebook
* IDIdentifier class inherited from CausalIdentifier
* Added initial code for causal discovery example notebook
* Used CDT library for causal discovery. NN based methods not tested yet.
* Made notebook for the Auto-MPG and Sachs dataset
* Added graph refutation to causal discovery example notebook
* Added textual descriptions to causal discovery notebook
* Modified confounder interpreter to work on categorical input and corrected a bug with assigning treatment variable
* Added interpreter example notebook
* Added descriptions for interpreters
* Rectified the SettingWithCopyWarning in confounder distribution interpreter
* Reduced beta to reduce skew in treatment variable
* added estimating_instrument_names support to econml and updated placebo refuter for iv methods.
* fixed bugs and added it to the iv notebook
Signed-off-by: Amit Sharma <amit_sharma@live.com>
* updated dummyoutcome and identifier minor bugs, updated hotel notebook
* causal graph does not add U by its own and identifier does not show all backdoor by default
* updated hotel notebook analysis and removed logging.info from 3 others notebooks
* Updated EconML names and argument parsing.
* Changed EconML namespaces to match v0.8.1. Further name
changes will be implemented in v0.9
* Updated argument parsing. As of v0.9, some `fit` arguments will be
passed in by keyword only
* Updated tests and example notebooks to be compatible with the latest
EconML changes
* Fix Windows compatibility of example notebook
* Point econml dependency to specific branch to debug build failures.
* Account for EconML internal features
* EconML concatenates the common causes W and effect modifiers X
internally so no need to do it explicitly in dowhy.
* EconML has better support for Pandas dataframes so I removed the
casting to numpy array.
* updated econml integration with econml library update
* metalearners are passed the union of common causes and effect modifiers
* Conditional-effects notebook has the new EconML method names
* fixed econml bug for CI intervals,
* removed input prompt from notebooks to enable automatic testing
* added tests for notebooks
* added nbformat, jupyter as req for github actions
* adding jupyter as dependency for workflow runs
* modified readme to be compliant with twine requirements
* updated version to 0.5
* updated docs toc source files, updated notebooks markdown headers, and some docstrings in dowhy code
* updated information about new release
* Added Example Notebook.
Example Notebook demostrating how to use Dowhy to assess the impact of assigning a different room on Hotel Booking Cancellations.
* added support for mediation effects: natural direct effect and natural indirect effect
* Identification is non-parametric, supports confounders for first-stage and second-stage
* Estimation is using a linear model over two stages. TwoStageRegression.
* updated frontdoor code to support common causes of first stage and second stage.
* added jupyter notebook
* fixed test errors
* Backdoor implementation now searches over all possible adjustment sets
* Returns a list of admissible adjustment sets, along with a default (the default is to show the set that covers the maximum number of backdoor paths, and conditions on the maximum number of variables--optimizing for low bias instead of efficiency.
* Changed data structure of IdentifiedEstimand to reflect the above
* As a usability improvement, improved the str output for estimator and identifier
* Updated tests to reflect the changes in identification
[Work in progress]
Support for interpretation of the different objects returned in the dowhy 4-step process.
* CausalModel.interpret()
* CausalEstimate.interpret()
* CausalRefutation.interpret()
Base classes defined for an interpreter, and the two main types of interpreters: TextualInterpreter and VisualInterpreter.
Example implementations of a simple TextualInterpreter that conveys the meaning of an effect estimate, and a Visual PropensityBalanceInterpreter that produces a balance plot for propensity score stratification estimation method.
* added cate estimator to causal estimator and one specific estiamtor for linear regression
* Started with a better error message for estimators that do not support CATE as yet, but then generalized to add error messages for when any specific method is not implemented for an estimator.
* added docstrings for the new functionality
* added a warning if effect modifiers provided are extra from the ones specified before, and fixed a bug for discrete variables
* updated files in prep for release of v0.4
* Effect modifiers can be common causes too. When that occurs, graph shows them as a common cause but they are separately recorded as an effect_modifier too.
* This means that even with graph input to causal model, effect modifiers can be specified.
* updated econml estimator to show a warning whenever x is not a subset of w
* add the ability for custom functions to create dummy outcome
* replace outcome_prediction_function by outcome_function
* fix spelling mistakes in dummy_outcome_refuter
* add type check support
* add test for dummy_outcome_refuter
* example for dummy outcome refuter
A detailed example on how to use the dummy outcome refuter.
* rename the file to match naming convention
* add support for pandas series and dataframes
* add missing pandas dependency
* rename the outcome function
* change the title of the notebook
* convert the title to Camel Case
* fix the p values in the demo
* undo p-value change and fix spellings
Peter Götz
Amit Sharma
Peter Goetz
anusha0409
Siddhant Haldar
Baran Toppare
Lovkush
Miruna Oprescu
Arshia Arya
Siddharth Dikshit
Tanmay Kulkarni
James Fiedler
adam
Sonu Mehta
AndreaChlebikova
Yadnyawalkya