Questions tagged [causality]
The relationship between cause and effect.
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Are boosted machine learning methods robust against low probable feture 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 ...
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0answers
9 views
Ok to use PSM to create treatment groups and then plug into CausalImpact? [on hold]
Is it ok to use propensity score matching to create treatment and control groups and then plug these two time series into CausalImpact to estimate your treatment effect? I might want to do this, for ...
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10 views
Two-step residualization in fuzzy RD
I'm adopting a two-step residualization approach in fuzzy RD, should my first stage also include a polynomial of running variable (age)?
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17 views
Learning Causal Graph from data
I am quite new to the theory of causal graphs, but from what I understand they are DAG, like Bayesian Networks. Since we have structure learning methods for Bayesian Networks like score based ...
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23 views
How do I model this DAG
I am new to the world of DAGs and in a way I am fascinated by these concepts. So I decided to solve a real world problem which is as follows. I apologize for a lengthy post.
$Y$ my outcome variable ...
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0answers
24 views
Is causal relationship between two variables a theory?
I was asked if Y causes Z a theory, and I noticed that I have a gap in my knowledge on that. I know that theory could be causal, descriptive or predictive in its explanation. However, a theory is an ...
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24 views
Can I take all variables used for correlation analysis for future multiple regression analysis?
I have sample size of 120 persons and I have 15 independent variables and 1 dependent variable. I previously used correlation analysis found 9 of them are moderately to highly correlated. I want to ...
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1answer
27 views
Causal inference: is it possible to estimate the actual users who did X because of Y?
Suppose we have a dataset with many (~100) features and a binary outcome. I am interested in not only assessing whether any given feature is causally related to the outcome, but in actually being ...
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43 views
Causality: Structural Causal Model and DAG
I know that in general a structural causal model (SCM) can be write in term of structural equations. Then in more qualitative but however formal manner we can rewrite the model in term of DAG. Now we ...
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1answer
13 views
Directly take the difference of the metric between 2 groups after the treatment or doing a difference-in-difference?
Assume we are doing a randomized experiment. The dependent variable is $Y$. Usually, we randomly put some of the subjects in the treatment group ($n$ subjects) and the rest to the control group ($m$ ...
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1answer
35 views
Expectation of potential outcomes formula
In Mostly Harmless Econometrics, the author uses the following identity to derive an estimator for the causal effect:
$$E \left[ \frac{Y_i D_i} {p(X_i)} \right] = E \left[Y_{1i} \right]$$
where:
$...
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13 views
Proving that average treatment effect for RDD with multiple cutoffs is a weighted average of the causal effect at each cutoff
Let $Xi$ denote the original running variable and $Ci$ be the cutoff that unit $i$ faces (e.g., the nearest cutoff).
For simplicity, I consider a case where the binary treatment is assigned if $Xi$ ...
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15 views
What is the difference between the estimand of sharp and fuzzy regression discontinuity design?
I would like to know:
1- the difference between the estimand in sharp and fuzzy regression discontinuity design (RDD).
2- the additional assumptions needed to identify the estimand in fuzzy RDD.
...
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4answers
67 views
How to form groups before randomizing the treatment assignment?
Consider the following example. You want to assess the effect of a new class program on educational outcomes relative to the old program. You have recruited $N$ subjects for a randomized control trial ...
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2answers
182 views
Argument on Interactions in The Book of Why
There is a paragraph on interactions in The Book of Why (Pearl & Mackenzie, 2018), Chapter 9 (I cannot share the page number because I have the book in epub format), where the authors argue that:
...
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9 views
Finding causation in data with Conditional entropy
Conditional entropy is defined here. I was wondering about an algorithm to find causality between random variables. In particular, I want to calculate $H(X|Y)$ and $H(Y|X)$ and make a guess as to ...
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1answer
119 views
How to perform a what-if study using observational data?
A team fit a Random Forest model to a dataset $S=\{\mathbf{x}_i,y_i\}_{i=1}^N$, where $\mathbf{x}$ is a vector of continuous and categorical variables, and $y$ is a binary response. The model has a ...
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19 views
experimental design to test the effects of price discount
I have a panel dataset with hourly sales：(0:00~23:00) by products, after 16:00, the platform will give a discount to several products if they monitor a high inventory for these products.
I want to ...
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1answer
26 views
How to generate a model for the causal effects for a Panel dataset
I have a dataset such as hourly sales data：(0:00~23:00) by products, after 18:00, the platform will give a discount to several products to speed up the sales. If I want to know the causal effects of ...
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2answers
174 views
Does direction of causality between instrument and variable matter?
The standard scheme of instrumental variable in terms of causality (->) is:
Z -> X -> Y
Where Z is an instrument, X ...
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0answers
41 views
The impact of other continuous variable on the DID estimate
I have searched around but didn't have any luck. I am trying to estimate and then compare the DID estimates of the effect of a Treatment on Life-expectation across different Income levels, to know if ...
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0answers
17 views
Why do stabilized IPW weights give the same estimates and SEs as unstabilized weights?
In Cole & Hernán (2008), the authors mention that using stabilized weights can decrease the variance of the effect estimate. Regular inverse probability weights use the probability of being in the ...
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14 views
Causality in variance test, Cheung and Ng (1996)
I have been reading about the Cheung and Ng (1996) causality in variance test.
Would anyone know of any code or instructions on how I can code it up.
I have heard it is pretty simple, but I havent ...
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1answer
25 views
Are there any examples of multinomial or logistic regression as an outcome model using propensity score weighting?
I apologize if this is an inappropriate question, but does anyone have more recent texts on implementing some type of covariate balance weighting scheme (entropy balancing, IPTW, etc) where the ...
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1answer
90 views
Third Newton's law as a DAG [closed]
A we all know, Newton's third law is:
For every action, there is an equal and opposite reaction.
So, If A is pushed with force K by B, then it pushes back B with force k.
How can I represent this with ...
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1answer
46 views
Can causation be inferred when all possible covariates are included in a multiple regression?
Say we were interested in SAT scores for high school students as our dependent variable in a multiple regression. Now, assume we are God and can include literally all relevant covariates in the model (...
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19 views
When to use an interaction with dummy variables, and when to estimate separate regressions? [duplicate]
I am interested in exploring heterogeneous treatment effects by category. As a simple example, imagine that I'm trying to predict the impact of job training on income in New York, and I want to ...
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0answers
36 views
Exploring Causal Relationship in R
I need to explore if there are any causal relationships in my dataset, but the data has 30 points per year (for 30 different countries) and only 10 years worth of data. Accordingly, I can't use ...
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1answer
35 views
sum rule in conditional probability
P and S are the common cause of c. If P(C=true| P,S ) is given , can I introduce S to P(C|P) as P(C= true|P= true)= P(C=true| P =true , S= true)* P(P=true ,S=true )+ P(C= true | P=true ,S =false )*P(P=...
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1answer
52 views
How to eliminate variable given conditional probabilities
$P$ and $S$ are the common cause of $c$. If $P(C=true| P,S )$ is given as the table below, and $P(S=true) =0.3$, $P(P=true) =0.9$ how can I eliminate $S$ and calculate $P(C=true | P=true )$ and $P(C= ...
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0answers
51 views
Dealing with Endogeneity in a Logit Regression when the Endogenous Regressors are Discrete
I would like to estimate a logit model in the presence of endogeneity. The dependent variable is binary (actually, it is non-binary with multiple ordinal categories, but from what I've read dealing ...
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33 views
Are feature importances from tree based models directly actionable for business?
If my response variable say is "has_repurchased" [0 or 1] and I have all customer level features. Can I rank the features in order of importance from the random forest model and report them as whats ...
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14 views
For Granger Causality, what inference can be made if p is < 0.05 for ssr based chi2 test, but larger for everything else for a specific lag?
I am running a Granger causality test using statstools in Python, but am struggling to interpret the results correctly. It is my understanding that if the p value < 0.05, one can assume high ...
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0answers
36 views
IPW for the effect of treatment on treated with a continuous treatment
I've been banging my head against the wall trying to figure out how to construct inverse probability of treatment weights (IPTW) for the population average effect of treatment on treated (PATT) with a ...
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2answers
30 views
For the Average Treatment Effect (ATE) in causal inference, defined as $E(Y(1) - Y(0))$, what is $E(Y(1))$ usually referred to as?
For the Average Treatment Effect (ATE) in causal inference, is it usually defined as
$$
E(Y(1) - Y(0))
$$
I am wondering what the most commonly referred to name for $E(Y(1))$ is? Is it not the ...
1
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1answer
29 views
Does the t-test require randomization?
Can I use t test for a non-equivalent quasi-experimemtal design? As there is no randomization, can it violate the assumptions of the t-test? What statistical technique should I use?
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1answer
20 views
In the potential outcomes framework, does conditioning on the potential outcomes automatically imply knowledge of the treatment assignment?
Suppose we have that $Z\in \{0,1\}$ is the treatment, $(Y(1),Y(0))$ the potential outcomes, and $X$ the covariates. Suppose we have know that unconfoundedness holds on $X$, such that:
$$
(Y(1),Y(0)) \...
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0answers
32 views
Review paper on causal modelling of complex networks
Although I have a growing interest in network neuroscience and complex neuroscience in general, I have quite a bit of trouble following Twitter discussions on causal modelling of brain network ...
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0answers
16 views
In causal inference, is the usual unconfoundedness assumption interpreted to apply at the unit or covariate level?
Suppose for each unit $i \in \{1, \ldots, N\}$, we have that
$(Y_i(1),Y_i(0))$ are the potential outcomes,
$Z_i$ is the treatment,
and
$X_i$ the covariates.
I have seen the following two ...
0
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2answers
41 views
If unconfoundedness holds under a set of covariates $X$, will it also hold on an extended set of covariates, $(X,X')$?
Suppose unconfoundedness holds for a set of potential outcomes $(Y(1),Y(0))$ and treatment $Z$, conditional on a set of covariates, $X$ such that:
$$
(Y(1),Y(0)) \perp Z \mid X
$$
Then, is it ...
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1answer
28 views
In Exact Matching in Causal Inference, why is it that that $P(X_i=x\mid Z_i = 1)= P(X_i=x\mid Z_i = 0)$ where $X,Z$ are the covariates and treatment?
In Exact Matching in Causal Inference, I read that because we assume exact matches, then exact balance occurs in the distribution of the covariates. It is then often stated that if $X,Z$ are the ...
1
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1answer
22 views
Propensity score: which treatment effect is easier to infer?
I'm currently working on a study where the goal is to estimate the treatment effect of a binary exposure. I want to calculate the Average Treatment Effect (ATE), Average Treatment Effect in the ...
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0answers
33 views
conditional and interventional expectation
Conditional expectation $E[Y|X]$ and interventional expectation $E[Y|do(X)]$ are related but conceptually very different things.
I know that if $X$ is a randomly assigned by an experiment, we have ...
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1answer
101 views
Using Machine Learning to Estimate Causal Effects from Observational Data
I would like to use machine learning to predict a categorical (ordinal, multi-class) outcome variable from a cross-sectional dataset with about 20,000 observations and 300 features. Importantly, I ...
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1answer
19 views
What's the difference between a “surrogate metric” and a “proxy metric”?
Is there a difference between a "surrogate metric" a "proxy metric" or a "correlated metric"? Is "surrogate" simply unnecessary jargon, or is there a meaning that makes it more specific than when ...
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0answers
13 views
How to choose the control variables in the conditional expectation to hold fixed when studying a causal relationship
I'm reading the introductory chapter of the wooldridge's book, "Econometric analysis of cross section and panel data". The chapter begins by highlighting the role and importance of conditional ...
2
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1answer
44 views
Is there an R package that can infer a causal structure with a mix of discrete and continuous variables? [closed]
I have an observational dataset with a mixture of discrete and continuous variables. I'd like to infer a causal structure compatible with the data.
The pcalg package for R can handle datasets with ...
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0answers
25 views
Kernel-based Propensity Score Matching diff-in-diff
I want to perform the Kernel-based Propensity Score Matching diff-in-diff. I am actually using the following command. The diff-in-diff result for this code is 0.000. Please, would someone help in ...
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1answer
69 views
What does it mean to “non-parametrically” identify a causal effect within the super-population perspective in causal inference?
I am wondering, within the context of causal inference, what it means to "non-parametrically" identify a causal effect within the super-population perspective. For example, in Hernan/Robins Causal ...
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1answer
90 views
Matching with Multiple Treatments
What's the best way to use matching methods with multiple treatment groups?
I'm assessing the impact of an intervention on an outcome. For my first analysis, I used the MatchIt package (see code below)...
