All Questions
Tagged with predictive-models causality
31 questions
70
votes
10
answers
66k
views
What is the difference between prediction and inference?
I'm reading through "An Introduction to Statistical Learning" . In chapter 2, they discuss the reason for estimating a function $f$.
2.1.1 Why Estimate $f$?
There are two main reasons we ...
- 801
32
votes
9
answers
8k
views
When can correlation be useful without causation?
A pet saying of many statisticians is "Correlation doesn't imply causation." This is certainly true, but one thing that DOES seem implied here is that correlation has little or no value. Is this ...
- 423
18
votes
2
answers
1k
views
How would econometricians answer the objections and recommendations raised by Chen and Pearl (2013)?
In their article, Chen and Pearl (2013), critically examined 6 econometric textbooks, among these the textbooks written by Wooldridge (2009) {the introductory book}, and Stock & Watson (2011). ...
- 4,792
13
votes
2
answers
15k
views
What is the relation between causal inference and prediction?
What are the relationships and the differences between causal inference and prediction (both classification and regression)?
In the prediction context, we have the predictor/input variables and ...
- 19.8k
12
votes
3
answers
2k
views
T-consistency vs. P-consistency
Francis Diebold has a blog post "Causality and T-Consistency vs. Correlation and P-Consistency" where he presents the notion of P-consistency, or presistency:
Consider a standard linear regression ...
- 69.5k
10
votes
7
answers
929
views
Bias-Variance tradeoff in prediction versus causal inference
In prediction, accepting a little more bias in exchange for a lot less variance is the very name of the game - we'll chose the model with minimal test MSE without regard for its composition (bias ...
- 4,792
10
votes
1
answer
849
views
How fair is it to use the word "predict" for (logistic) regression?
My understanding is that even regression does not give causality. It can only give association between y variable and x variables and possibly a direction. Am I correct? I've often found phrases ...
- 711
6
votes
2
answers
168
views
Is it possible to evaluate causal algorithms on real world observational data?
Lot of times I get asked to use causal algorithms (e.g. algorithms estimating intervention results, or in general causal inference algorithms) and to compare them against non-causal prediction ...
- 460
6
votes
1
answer
315
views
Causal implications from a model with poor predictive capabilities
I am currently reading McElreath's Statistical Rethinking book. I'm still in Chapter 5 though. I am unsure if this will be addressed later on but I can't help but wonder:
Earlier in the book, he ...
- 491
4
votes
3
answers
12k
views
What is the difference between correlation, causation and prediction?
Suppose we have a set of events $\Omega$, containing events $A$ and $B$. My econometrics professor tried to distinguish the following three terms today.
Causation --- $A$ causes $B$ if the ...
- 4,453
4
votes
2
answers
724
views
Including Collider Variables in Prediction
When the goal is to estimate a causal association between X and Y in the regression framework, one should not condition on (include as covariates) collider variables (common causes of both X and Y) ...
- 152
4
votes
1
answer
38
views
When is the knowledge of the causal mechanism useful for pure prediction?
In many settings, we are only interested in building a good predictor: e.g. $E(y_t | x_{t-1})$, where $y_t$ and $x_{t-1}$ are vectors of arbitrary dimension.
However, sometimes we are also given, or ...
- 348
4
votes
0
answers
807
views
How to determine the most important variables when there are differences in variable importance between predictive models [closed]
Background:
I am running predictive models to see which variables have the most influence over a chosen measurement. In this example I am comparing the models gbm ...
- 207
3
votes
2
answers
178
views
Should predictive analysis be tackled with causal inference in mind?
Say I am trying to predict depression from anxiety. I collect data and build a MLE and obtain r=0.9. To me, this is great, so I push the model to production. 4 months later, I realise that the "...
- 33
3
votes
0
answers
1k
views
Regression: Causation vs Prediction vs Description
In my experience it seems me that the interpretation about regression, its meaning and its scope, are debatable and great confusion exist about those things. It seems me that confusions are not go ...
- 5,779
3
votes
0
answers
820
views
Implication / Interpretation of long term equilibrium VECM
I want to test the influence of exchange rates on a price index and struggle with the interpretations. My variables are I(1)
First, I ran an OLS on first differenced variables which indicated a ...
- 31
2
votes
1
answer
54
views
Determine treatment effect based on conditional factors
Say I have an experiment running where I give treatment to a random selection of people, and I know that the target variable is affected by a number of factors. I know most of the factors that affect ...
- 498
2
votes
1
answer
41
views
R=X*Y is the relationship. Is predicting R and X and obtain Y same as predicting X and Y to obtain R?
Of course the numbers will be different, I mean more in terms of relationship. I know that X affect R and Y affects R . X and Y are independent but since R is a product of X and Y , I dont think that ...
- 71
2
votes
1
answer
152
views
Are predictions obtained with spuriously correlated predictors any useful?
Short version:
How useful are predictions of a variable y that are obtained using theoretically unrelated variables X that happen by mere luck to predict y very well? Is there any paper out there ...
2
votes
0
answers
173
views
Confusion on Ablation test (Ablation Experiment or Ablation study)
I followed the steps of the ablation test to calculate the feature importance one by one.
In Table 1, row 1 presents the model prediction performance of using full features. Regarding rows 2-4, these ...
- 21
1
vote
1
answer
38
views
Using predicted outcomes to adress selection bias in causal inference
Can I use predicted outcomes from one model as the dependent variable in another model to make causal claims? Put differently, is there something equivalent to the Frish-Waugh-Lovell theorem for ...
- 113
1
vote
1
answer
76
views
How to do analysis of correlation from multiple-timepoints measurements?
My case is analyzing the association between the concentration of HIV DNA prior to therapy (time point 0, $t_0$) and the concentrations of biomarkers of HIV infection after therapy, measured in 6 time ...
- 13
1
vote
0
answers
54
views
build and evaluate prediction model with the same data
I have a dataset with a sample size of n=30, one dependent variable and 31 possible predictors.
Now I want to build a regression model as part of a regression kriging model to predict my dependent ...
- 41
1
vote
0
answers
32
views
Are boosted machine learning methods robust against low probable feature combinations when predicting?
I would like to use machine learning methods in the potential outcome framework, that is, simulating outcome for all observations under different values of a specific predictor, while keeping all ...
- 2,939
1
vote
0
answers
2k
views
Evaluating results of VAR (Vector Autoregression) using R
I am trying to evaluate the results of a prediction obtained with the R function VAR. I have reproduced an example with two time series so that others can also implement it (the data set is read from ...
- 221
1
vote
0
answers
219
views
Binary logistic regression - SPSS
I did some regression analysis in SPSS using two binary variables:
Biomarker X (0= low levels; 1= high levels), where 0 was the reference category and Obesity (0=no; 1=yes)
''Biomarker X'' was taken ...
- 23
1
vote
0
answers
126
views
Do you need causal models when doing counterfactual predictions?
I am modeling the impact the number of a certain type of company (bottom of pyramid (BOP) companies, ie. companies that cater to the poorest consumers) have on market price. I considered the ...
- 11
1
vote
0
answers
299
views
Counterfactuals for Variables with Negative Values
Lets imagine I have estimated the following simple linear regression model:
$y_{i} = 10 + 0.5x_{i} + \varepsilon_{i} $, and want to work out the counter-factual, or what would $ y_{i}$ be in the ...
0
votes
0
answers
18
views
Substitution modeling under simulated inventory constraints
In the context of retail, substitution is typically defined as "when customers that prefer product A, but when unavailable, purchase product B, instead."
Naively, the most of useful ...
- 3,520
0
votes
0
answers
18
views
Extrapolating/forecasting treatment effects from difference-in-difference model
My goal is to extrapolate or forecast dynamic treatment effects into the future using a fitted model. My data consists of two groups (treated and control), seven time points, and a continuous outcome.
...
0
votes
1
answer
194
views
Difference between Predictive Inference and Causal Inference
I am looking for functional mathematical notation to explain the difference between Predictive Inference and Causal Inference? I list an example model. I also list links further down that give ...
- 1