Tagged Questions

Causal inference tries to quantify the effect of a change in $X$ on $Y$ whilst holding constant or eliminating all other relevant factors which might influence this relationship.

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6
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2answers
391 views

Understanding d-separation theory in causal Bayesian networks

I am trying to understand the d-Separation logic in Causal Bayesian Networks. I know how the algorithm works, but I don't exactly understand why the "flow of information" works as stated in the ...
2
votes
0answers
96 views

Causality in Time Series

I am reading an article which is trying to justify the need for causal inference in their inferential framework. The thought experiment is as follows: Suppose a statistician is asked to design a ...
3
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0answers
26 views

Adjusting for experimentally-caused panel attrition when evaluating treatment effects

This question involves a questionable hypothetical scenario, but please bear with me. Suppose I ran an experiment in a coffee stand where the treatment was playing country music instead of the usual ...
1
vote
0answers
282 views

Difference-in-difference in panel data

Under which conditions should we expect the difference-in-difference estimate to be equal to the equivalent panel data model? Strictly speaking, whenever we have a experiment that offers a well ...
3
votes
3answers
240 views

Mathematical definition of causality

Let $Y$ and $X$ be random variables. $E(Y|X)$ is the conditional mean of $Y$ given $X$. We say $Y$ is not causally related to $X$ if $E(Y|X)$ does not depend on $X$, which implies it is equal to ...
1
vote
1answer
104 views

Weighted Wilcoxon ranksum test

I am using Stata for a survival analysis project involving inverse probability weighting (IPW). The question has arisen as to how to analyze weighted continuous data between two groups with a Wilcoxon ...
3
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2answers
533 views

How do difference-in-difference designs account for temporal autocorrelation

Although there are doubtless many techniques for studying the impact of a discrete intervention over time, I am interested in two which have achieved widespread adoption in the social sciences: ...
4
votes
2answers
520 views

Does regression analysis measure cause and effect?

Does regression analysis measure cause and effect? If yes, then how? If no, then what is done? Please describe with an example.
3
votes
2answers
101 views

What to do in logistic regression if you have a huge amount of variables?

I am dealing with logistic regression, trying to identify variables which have a causal relationship with a binary response. The way I usually do it is to try variables one by one and visualize the ...
2
votes
0answers
48 views

Roy model question

I am referring to G.S. Maddala: Limited Dependent and Qualitative Variables in Econometrics, pages 257-258. I add the relevant screenshots here: My question is, why is ...
4
votes
1answer
181 views

Are the relations in fixed, random and mixed effect models and multilevel models causal?

In fixed, random and mixed effect models, and multilevel models, the response random variable is represented as a function of some explanatory variables and random errors. I was wondering if the ...
6
votes
4answers
179 views

Online resources for philosophy of causation for causal inference

Can you recommend any books, articles, essays, online tutorials/courses, etc that would be interesting and useful for an epidemiologist/biostatistician to learn about the philosophy of ...
0
votes
1answer
75 views

Finding the corresponding bayesian network of a predefined joint probability distribution

Given a joint probability distribution over the variables $X_1,X_2,\dots,X_n$. Is there an algorithm for constructing the corresponding Bayesian Network?
2
votes
1answer
66 views

Are latent variable models modelling causality?

Is the purpose of latent variable models to model causality, where the causes are not observable i.e. latent? Are latent variables modelling causes of the observable variables? Thanks and regards!
0
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1answer
43 views

Terminology to Use

Should you say a factor is associated with a disease or disease risk? For example, which is better to say: Smoking is associated with lung cancer Smoking is associated with lung cancer risk
1
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0answers
541 views

Econometrics: Sargan test

Here are 3 questions about econometrics and R codes. Test the endogeneity of the variable EDUC: ...
1
vote
1answer
316 views

What test is this for endogenous variables?

Can somebody tell me whether the following R code (for econometrics endogenous variables) is for a Hausman test, a Nakamura test, or some other test? ...
0
votes
1answer
658 views

What does plausibility or plausibile mechanism mean?

In Bradford Hill criteria for causality Plausibility: A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge). ...
9
votes
1answer
138 views

Choice of path weights in SEM conceptual model using openMx

I am reviewing the R package OpenMx for a genetic epidemiology analysis in order to learn how to specify and fit SEM models. I am new to this so bear with me. I am following the example on page 59 of ...
3
votes
1answer
83 views

Multiple imputation for variables used to calculate regression weights

My basic question: is there anything that you can't impute using MI? My more complicated question: Consider the regression $Y=\rho T+X'\beta+\epsilon$. For whatever reason, you want to weight the ...
8
votes
1answer
166 views

Properties of bivariate standard normal and implied conditional probability in the Roy model

Sorry for the long title, but my problem is quite specific and hard to explain in one title. I am currently learning about the Roy Model (treatment effect analysis). There is one derivation step at ...
0
votes
1answer
102 views

How to quantify mis-specification bias and compare against smoothing bias for a non-parametric estimate of a randomly allocated continuous treatment?

Suppose that there is a data-generating process $$ y = \alpha + g(x) + \epsilon $$ which is to say that an outcome is some function of $x$. Suppose that $x$ is randomly assigned, so ...
8
votes
4answers
6k views

What do “endogeneity” and “exogeneity” mean substantively?

I understand that the basic definition of endogeneity is that $$ X'\epsilon=0 $$ is not satisfied, but what does this mean in a real world sense? I read the Wikipedia article, with the supply and ...
5
votes
1answer
79 views

Causal identification and penalized splines

I just got a rejection from an economics journal. Among the reasons cited for rejection were: the benefits of using the semi-parametric method are not clearly brought out compared to ...
2
votes
1answer
26 views

Choice of referent twin in twin difference model

Carlin (2005) points out that mixed effects models specifically for twin data can be simplified by calculating differences between paired clusters. This allows for modeling specifically the within ...
1
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0answers
72 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 ...
3
votes
2answers
545 views

Relation between causal inference and prediction (classification and regression)

I was wondering what relation and differences are between causal inference and prediction (classification and regression)? For example, In prediction, we have predictor/input variables and ...
2
votes
1answer
71 views

Relative efficiency of matching versus adjustment to handle confounding effects in highly disproportionate populations

I am reviewing a paper where the authors compare cancer outcomes (binary) between two groups, one having a small sample size of 200 and the other having over 55,000. The authors then claim that, due ...
2
votes
2answers
218 views

Causality, omitted variable bias

This might be a basic question, but I want to be sure that what I'm doing is right. I have a model that suggests that variable X causes both Y and Z. When I regress Y on X, or Z on X, I get positive ...
3
votes
1answer
96 views

I have GBs of Event-Based Data. How do I figure out causation?

I have a lot of event-based data about users of our website. For example, data in the format (verb, timestamp). There's about 10 or so different verbs (call them A, B, C, etc). I'm interested in ...
0
votes
2answers
472 views

Do we need Overlap/Common Support in case of a parametric regression?

If I want to make a causal statement based on selection on observables. One typically assumes "Common Support" (/"Overlap") - which means that for any value of the confounding variables X a unit i can ...
1
vote
0answers
470 views

An alternative to “Granger Causality” test when (short) time series are not stationary?

I have two short time series (x and y), and I wish to find out if x "effects" (is correlated with) y. Obviously, since the two are time series, using a simple correlation is the wrong way to go. I ...
5
votes
3answers
496 views

Big Data vs multiple hypothesis testing?

Nate Silver in his excellent "The Noise and the Signal" warned that we are much in awe of Big Data. But, that Big Data predictions in many fields have been disastrous (financial markets and economics ...
-2
votes
1answer
266 views

Causality test for logistic regression

For time series there is the Granger causality test. Is there some causality test for the logistic regression?
1
vote
1answer
959 views

Formula for one-sided Hodrick-Prescott filter

I am not very familiar with filters. The Hodrick-Prescott filter as one can find it e.g. in wikipedia is two-sided. I also found an R implementation for this in the R package mFilter. There the filter ...
4
votes
1answer
123 views

Why arrange variables by causality in bivariate regression?

Suppose we have variables $(X,Y)$ and we have theory tell us that $X$ $\overset{\text{cause}}{\implies} Y$. Perhaps they're time-series variables and it would be common to see something like this: ...
1
vote
1answer
445 views

What if only control variables are significant in a differences-in-differences analysis?

Regarding the standard DID model: $$ y=\alpha+\beta_1\text{treat}+\beta_2\text{post}+\beta_3\text{treat⋅post}+u $$ What exactly does it mean if say $\beta_3$ is not statistically significant, but ...
14
votes
3answers
307 views

Introduction to causal analysis

What are good books that introduce causal analysis? I'm thinking of an introduction that both explains the principles of causal analysis and shows how different statistical methods could be used to ...
1
vote
2answers
89 views

Causal stats with one event and multiple time series?

I've worked with certain causal/predictive techniques when handling two time series, but this problem is different from what I am used to and I'm not sure how to proceed. I would like to see the ...
3
votes
2answers
435 views

What test should I use to determine if a policy change had a statistically significant impact on website registrations?

A client's website was operating under a certain policy for membership sign ups for over a year. At the start of October 2012 the client implemented a new policy for sign ups that was supposed to ...
4
votes
3answers
543 views

Fuzzy regression discontinuity design and exclusion restriction

In a fuzzy regression discontinuity design, what does the exclusion restriction look like in terms of a conditional expectation between the instrument in the first stage and the error term in the ...
1
vote
1answer
224 views

How to account for a regressand affecting a regressor?

I forget the terminology, but this happens when you regress, say, $Y$ on a list of variables, and you suspect that $Y$ affects, say, $x_3$ in addition to $x_3$ affecting $Y.$ I forget how this is ...
4
votes
2answers
194 views

Causation implication

I recently read an article about how you can increase longevity by sleeping less. This article, like many others I've read, references a statistical study and implies that causation was found between ...
0
votes
2answers
2k views

Proving Causality with t-test/regression

Earlier today I was discussing statistical analysis software with a colleague of mine. My colleague had primarily used SPSS in previous work for performing t-tests, anovas, manovas, and other ...
3
votes
2answers
674 views

Effect of one independent variable of several dependent variables – best strategy?

I have a question regarding which analysis strategy is best suited for our objective. In an exploratory study based on data from a survey we conducted ourselves in India, we are analyzing the ...
3
votes
1answer
639 views

Heckman selection model with difference-in-differences specification

Following my question on Tobit with DiD specification I am wondering if it is possible to estimate a heckman sample selection model with a Difference in Differences specification? For example in ...
7
votes
1answer
631 views

Is it possible to have a variable that acts as both an effect modifier and a confounder?

Is it possible to have a variable that acts as both an effect (measurement) modifier and a confounder for a given pair of risk-outcome associations? I'm still a little unsure of the distinction. I've ...
8
votes
3answers
691 views

Random assignment: why bother?

Random assignment is valuable because it ensures independence of treatment from potential outcomes. That is how it leads to unbiased estimates of the average treatment effect. But other assignment ...
3
votes
2answers
393 views

How to avoid the problem of two-way causality?

I am studying the effect of social capital on households' income. I am doing multiple regression to estimate this effect. For this, I have households' income as dependent variable and social capital ...
4
votes
3answers
260 views

Formal definition of random assignment

I am looking for a formal definition of random assignment. Let $\mathbf{Z}$ be a vector of treatment assignments in which each element is 0 (unit not assigned to treatment) or 1 (unit assigned to ...