CausalELM enables estimation of causal effects in settings where a randomized control trial or traditional statistical models would be infeasible or unacceptable. It enables estimation of the average treatment effect (ATE)/intent to treat effect (ITE) with interrupted time series analysis, G-computation, and double machine learning; average treatment effect on the treated (ATT) with G-computation; cumulative treatment effect with interrupted time series analysis; and the conditional average treatment effect (CATE) via S-learning, T-learning, X-learning, R-learning, and doubly robust estimation. Underlying all of these estimators are ensembles of extreme learning machines, a simple neural network that uses randomized weights and least squares optimization instead of gradient descent. Once a model has been estimated, CausalELM can summarize the model and conduct sensitivity analysis to validate the plausibility of modeling assumptions. Furthermore, all of this can be done in four lines of code.
In some cases we would like to know the causal effect of some intervention but we do not have the counterfactual, making conventional methods of statistical analysis infeasible. However, it may still be possible to get an unbiased estimate of the causal effect (ATE, ATE, or ITT) by predicting the counterfactual and comparing it to the observed outcomes. This is the approach CausalELM takes to conduct interrupted time series analysis, G-Computation, double machine learning, and metalearning via S-Learners, T-Learners, X-Learners, R-learners, and doubly robust estimation. In interrupted time series analysis, we want to estimate the effect of some intervention on the outcome of a single unit that we observe during multiple time periods. For example, we might want to know how the announcement of a merger affected the price of Stock A. To do this, we need to know what the price of stock A would have been if the merger had not been announced, which we can predict with machine learning methods. Then, we can compare this predicted counterfactual to the observed price data to estimate the effect of the merger announcement. In another case, we might want to know the effect of medicine X on disease Y but the administration of X was not random and it might have also been administered at mulitiple time periods, which would produce biased estimates. To overcome this, G-computation models the observed data, uses the model to predict the outcomes if all patients recieved the treatment, and compares it to the predictions of the outcomes if none of the patients recieved the treatment. Double machine learning (DML) takes a similar approach but also models the treatment mechanism and uses it to adjust the initial estimates. This approach has three advantages. First, it is more efficient with high dimensional data than conventional methods. Metalearners take a similar approach to estimate the CATE. While all of these models are different, they have one thing in common: how well they perform depends on the underlying model they fit to the data. To that end, CausalELMs use bagged ensembles of extreme learning machines because they are simple yet flexible enough to be universal function approximators with lower varaince than single extreme learning machines.
- Estimate a causal effect, get a summary, and validate assumptions in just four lines of code
- Bagging improves performance and reduces variance without the need to tune a regularization parameter
- Enables using the same structs for regression and classification
- Includes 13 activation functions and allows user-defined activation functions
- Most inference and validation tests do not assume functional or distributional forms
- Implements the latest techniques form statistics, econometrics, and biostatistics
- Works out of the box with arrays or any data structure that implements the Tables.jl interface
- Codebase is high-quality, well tested, and regularly updated
- Now includes doubly robust estimator for CATE estimation
- All estimators now implement bagging to reduce predictive performance and reduce variance
- Counterfactual consistency validation simulates more realistic violations of the counterfactual consistency assumption
- Uses a simple heuristic to choose the number of neurons, which reduces training time and still works well in practice
- Probability clipping for classifier predictions and residuals is no longer necessary due to the bagging procedure
- CausalELM talk has been accepted to JuliaCon 2024!
Newer versions of CausalELM will hopefully support using GPUs and provide interpretations of the results of calling validate on a model that has been estimated. In addition, some estimators will also support using instrumental variables. However, these priorities could also change depending on feedback recieved at JuliaCon.
CausalELM is extensively tested and almost every function or method has multiple tests. That being said, CausalELM is still in the early/ish stages of development and may have some bugs. Also, expect breaking releases for now.All contributions are welcome. Before submitting a pull request please read the contribution guidlines.