Questions tagged [explanatory-models]
Models created to explain a response (as opposed to simply predict it). This is generally understood to imply models of causal processes, or to test hypothesized causal relationships.
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Appropriateness of using SHAP values to evaluate a model
I am a deep learning researcher that would like to use SHAP values to assess the relative importance of input features on the model's final score. Colleagues of mine have taken issue with the method ...
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interpret lime results
What is the meaning of the values of the "blue" features in the lime output? I understand that they influence the lime black-box model to classify an observation as 0 label, but what is the ...
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Use of simulation in explanatory versus predictive models
I understand the important distinction between explanatory vs. predictive models. However, I often read that simulations is an important tool in risk modelling and risk analysis. How are simulations ...
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How Expected Prediction Error and Parameter estimation error are related each other?
I grasp that the ultimate goal of regression is essential for proper optimization: If your goal is prediction, you must minimize the prediction error. Otherwise, if your goal is explanation, you need ...
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How explainable is Linear Discriminant Analysis?
In a survey paper on the interpretability of various machine learning algorithms, I didn't find Linear Discriminant Analysis (LDA). I wonder how explainable is LDA to audiences not familiar with ...
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(Explanatory) Modelling with PMG and MG estimators
Hello kind people of Stackexchange. I happen to be kind of stuck and in need of help. See, I want to make an ARDL model with PMG and/or MG estimator (Pesaran, 1999) (okay, in fact it may be the CCE ...
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Clarification the function of coefficient of Post variable in DID for two-year two-group
When reading a paper about DiD, I saw a sentence describing the function of the $Post_t$ variable in DiD for two groups and two periods:
In a similar manner, the coefficient on $P_t$ captures the ...
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Metrics for Explanatory Power of Machine Learning Classification Models
I am interested in finding metrics that assess the explanatory power of machine learning models involving binary classification. I know that for logistic regression, McFadden pseudo R squared, AIC, ...
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Explainable neural network through supervision of penultimate layer
Generally, most deep learning (DL) models are considered opaque black boxes, and post-hoc explanations may not be satisfactory to users, especially for use cases in the legal or clinical world where ...
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How to explain features' importance after PCA?
I had a dataset that was terribly haunted by colinearity. I carried out PCA on it to remove the colinearity. Let's assume I am going to build a linear regression model, which is easily explainable, on ...
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Why Feature Selection with sklearn.feature_selection.SequentialFeatureSelector is a preprocessing task?
I am facing a feature selection problem.
Because I am building an Explanatory Regression Model I decided to follow a Forward Sequential Feature Selection.
Moreover I wanted to implement ...
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X on Y Linear regression
Basically in a research project I am looking at the linear regression between my independent variable: Government Stringency Index, and dependent real GDP growth.
One area I investigate assumes if ...
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Assessing potential multicollinearity for various model types meant for causal inference [duplicate]
I was wondering if, in the case where I use the same set of predictors for different kind of models (e.g. ANOVA, Poisson GLM, logistic regression...), VIF values for each predictor would be similar ...
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Is there a way to increase the number of predictor in a logistic regression when sample size is small?
One of the dependent variables in my dataset is a binomial variable indicating whether an invasive species has been eradicated (with $n_{1}$ = 23) or not (with $n_{0}$ = 75) after the use of a certain ...
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Is a beta regression appropriate for a skewed bounded continuous dependent variable when sample size is small?
My data contain a bounded continuous variable (score between 0 and 10) representing the efficacy of a given method to control an invasive species. As there are more high scores than low ones, the ...
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Another term for "real time decisioning"
There is another term for "real time decisioning," (RTD) and I need it to locate a paper (by searching). I've been beating my head over this all day. I'm thinking it's "[something] ...
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Redundancy analysis (RDA) to identify the relationship between water quality and land use
I have read some papers in where the authors had performed redundancy analysis (RDA) to identify the relationship between water quality and land use. However, I am confused as to how they are setting ...
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Why do DeconvNet use ReLU in the backward pass?
Why does DeconvNet (Zeiler, 2014) use ReLU in the backward pass (after unpooling)? Are not the feature maps values already positive due to the ReLU in the forward pass? So, why do the authors apply ...
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Should I use cross-validation to build an explanatory model?
I know it is recommended to do so for a predictive model, but if I am trying to study a potentially causal relationship and adjusting for covariates to be able to isolate that relationship should I ...
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Reasons that LIME and SHAP might not agree with intuition
I'm leveraging the Python packages lime and shap to explain single (test-set) predictions that a basic, trained model is making on new, tabular data. WLOG, the explanations generated by both methods ...
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Is a predictive model the best way to generalize about my current data?
Suppose I have a customer dataset. My independent variables are various types of customer attributes (age, where they live, gender, price of the item they are potentially buying) and my dependent ...
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On George Box, Galit Shmueli and the scientific method?
(This question might seem like it is better suited for the Philosophy SE. I am hoping that statisticians can clarify my misconceptions about Box's and Shmueli's statements, hence I am posting it here)....
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Application of GAM on large dataset
I was suggested that my questions were too broad. As I commented below, I have nearly a million data points and perhaps a hundred variables. This may be a very basic modeling question: I am curious to ...
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What are the most important/seminal/popular methods in the field of interpretable ML?
As I've been trying to get a grasp on the field of interpretable ML I have encountered many interesting papers and methods, but I lack the perspective to determine their importance to the field as a ...
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In scale adaptation which should be done first? CFA or Reliability Analysis
I am adapting questionnaire cross-cultural. Questinonnaire has 11 item and two factor. I want to confirm the structure
1 item's corrected item total correlation is <.20. Should this item be ...
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Is this an appropriate way of modeling the scores in a round-robin sports league?
I want to model the outcome of matches in a round-robin sports league based on which home team is playing which away team across several seasons.
Let's assume a league with four teams ...
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Does cross validation say anything about parsimony?
Suppose I had a set of models that all attempt to explain some phenomena. According to a sensible—and appropriately cross-validated—performance metric, all of the models perform comparably well. The ...
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Standardization and explanatory variables of different domains in Multiple Regression
There's many questions on related topics but I have been unable to find one that precisely answers my question.
Let's say I'm performing a regression on multiple predictor variables $x_1...x_n$ for ...
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How does LIME compares with Mutual Information?
So, I was wondering how LIME's linear model approach compares with other explanation metrics, in special, with Mutual Information?
For those unfamiliar with how LIME works:
Choose the instance you ...
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General approaches and techniques for developing good explanatory models for nonlinear data
Various recent efforts of mine on modelling some data through logistic regression have been... not successful. While there is still more data to look at, I've been wanting to explore nonlinear ...
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Quantifying explanatory potential
Suppose I have a random variable $T_j \sim Bernoulli(p_j)$ where $logit(p_j) = \theta x_j + \epsilon_j$ and where $\epsilon_j \sim \mathcal{N}(0,1)$. Suppose further that $\theta = 0.018$ and that I ...
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Minimizing bias in explanatory modeling, why? (Galit Shmueli's "To Explain or to Predict")
This question references Galit Shmueli's paper "To Explain or to Predict".
Specifically, in section 1.5, "Explaining and Prediction are Different", Professor Shmueli writes:
In explanatory ...
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Is MSE decreasing with increasing number of explanatory variables?
I am wondering, if there is a negative correlation between Mean Squared Error
\begin{equation}
MSE = \frac{1}{n} \sum (\hat{Y}_i - Y_i)^2
\end{equation}
and the number of explanatory variables. ...
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How exactly does ridge regression helps in the case of multicollinearity?
I understand the reasoning behind ridge regression: we include some bias in the model in order to reduce the variance of the regression coefficients. My question is, why would we want to do that?
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Purpose of leave-one-out cross-validation in descriptive modelling
I refer you to Breiman's paper Statistical Modeling - A Tale of Two Cultures where he illustrated some examples of descriptive modelling. Under section 11.1, 100 runs of regression were performed, ...
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Is inference based on full (global) regression model appropriate?
Is inference based on a full model appropriate, and if so, in which circumstances?
Suppose you are interested in the potential relationship between a response variable and several candidate predictor ...
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Should I use unpenalized logistic regression, lasso or ridge for explanatory modelling?
When using logistic regression for predictive modelling, the choice between 'standard' logistic regression vs ridge vs LASSO versions of logistic regression seems relatively straightforward - just ...
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How to test predictive ability of an explanatory model?
As a part of my research I create an explanatory negative binomial regression model. Now, I want to show this model can also have predictability power. I don't want to compare my model with other ...
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Predictive power vs. explanatory power of statistical models
Is it possible for a statistical model to have explanatory power but no predictive power?
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Assess a model building technique
I am confused about a certain model building technique that seems to exist, at least in practice (I am not sure whether it has its place in textbooks). Question 1: I wonder under what conditions or ...
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LASSO for explanatory models: shrinked parameters or not?
I'm conducting an analysis where the primary goal is to understand the data. The dataset is large enough for cross-validation (10k), and predictors include both continuous and dummy variables, and the ...
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A bunch of different types of variables (their combination also important) explaining one variable - which method?
I have a dependent variable - how much land does a household cultivate out of total in their possession. The answers are categorized in 3 different groups (1 - 70% - 100%, 2 - 40 - 70%, 3 - less than ...
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Prediction vs. Explanation and its Effect on Statistical Methods [duplicate]
In layman's terms, what is the difference between predicting and explaining in statistics? I was looking for the differences between AIC and BIC and found this post with an answer stating:
My quick ...
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Predictive modeling techniques for in-sample rather than out-of-sample prediction?
Is it appropriate to apply predictive modeling variable selection and shrinkage techniques (for example, ridge regression or lasso) for in-sample prediction rather than out-of-sample prediction? ...
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