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.

learn more… | top users | synonyms

2
votes
3answers
123 views

How to rule out double causation

I have a model where I suspect (purely on theoretical grounds) that double causation might be an issue. How can I test this hypothesis? I.e. I have something like $Y_i = \beta_0 + \beta_1 X_i + ...
2
votes
1answer
72 views

Why is the conditional mean of the reduced form error zero?

For example, we have a simultaneous equation model of supply and demand: Supply: $$s(p)=\alpha_{s}+\beta_{s}p+\epsilon_{s}$$ Demand: $$d(p)=\alpha_{d}-\beta_{d}p+\epsilon_{d}$$ Market clearing ...
5
votes
1answer
94 views

Is there a branch of statistics that tries to explain “why” the dataset has certain statistical properties?

Suppose I have a big dataset and I compute some statistical summary of it - e.g., the correlation of one dimension with another. I think a reasonable question to ask would be "what data points ...
1
vote
1answer
46 views

How to refer to variables which lie beyond causal pathway

Causal diagrams are extremely good tools for discussing research plans for multivariate modeling between statisticians and non-statisticians. It's easy after some deliberation to decide which ...
2
votes
1answer
155 views

Omitted Variable Bias, verification in Gretl

I am trying to verify the expression for Omitted Variable Bias (OVB) as given e.g. in Wooldridge: $\tilde{\beta_1} = \hat{\beta_1} + \hat{\beta_2} \cdot \tilde{\delta_1}$, where $\tilde{\delta_1}$ is ...
0
votes
2answers
114 views

Correlation or t-test?

Following experimental design was done and some data as shown in table below was obtained: pretest> intervention1> intervention2> posttest > perception survey Number of students=60 Number of ...
9
votes
4answers
170 views

Do edges in directed acyclic graph represent causality?

I am studying Probabilistic Graphical Models, a book for self-study. Do edges in a directed acyclic graph (DAG) represent causal relations? What if I want to construct a Bayesian network, but I am ...
5
votes
1answer
137 views

Graphical models for correlation of random variables and prediction of hidden observations

I am studying about Graphical Models and I came up with a simple example but I am not sure which kind of technique (HMM, DGM, MRF) would be able to help me with that. Imagine we have three balls that ...
2
votes
2answers
113 views

Does randomization really allow us to make claims of causality?

Lets say two groups are compared. Subjects are randomly assigned to each group, then a treatment is given to half while a placebo is given to the other half. All aspects of the experiment (order of ...
0
votes
1answer
115 views

Two highly correlated variables where both correlate with a third: Correlation and Causation

My question is certainly quite basic for statisticians! Let's suppose Var1 and Var2 are highly correlated with a poor $R^2$. ...
2
votes
1answer
67 views

Interpretation of lasso recovery results

When people say that lasso regression can under certain assumptions recover "the support", i.e. non-zero regression weights, what does this mean? This cannot mean causal recovery, because Pearl has ...
6
votes
2answers
530 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
102 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
votes
0answers
30 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
339 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
247 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
120 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
votes
2answers
643 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
620 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
51 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
191 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
181 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
79 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
votes
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
vote
0answers
636 views

Econometrics: Sargan test

Here are 3 questions about econometrics and R codes. Test the endogeneity of the variable EDUC: ...
1
vote
1answer
359 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
813 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
148 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
93 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
169 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
103 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 ...
9
votes
4answers
7k 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
81 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
vote
0answers
75 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
631 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
76 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
225 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
97 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 ...
1
vote
2answers
527 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
490 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
528 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
281 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
1k 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
125 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
502 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
323 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
91 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 ...